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index 723c47948..9a4e34185 100644
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diff --git a/api/_images/tutorials_nihcxr_monitor_api_10_1.png b/api/_images/tutorials_nihcxr_monitor_api_10_1.png
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diff --git a/api/_images/tutorials_nihcxr_monitor_api_8_0.png b/api/_images/tutorials_nihcxr_monitor_api_8_0.png
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diff --git a/api/_modules/cyclops/query/ops.html b/api/_modules/cyclops/query/ops.html
index 3e381a66c..c62803741 100644
--- a/api/_modules/cyclops/query/ops.html
+++ b/api/_modules/cyclops/query/ops.html
@@ -3319,6 +3319,52 @@ <h1>Source code for cyclops.query.ops</h1><div class="highlight"><pre>
 
 
 
+<div class="viewcode-block" id="DropEmpty">
+<a class="viewcode-back" href="../../../reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty">[docs]</a>
+<span class="k">class</span> <span class="nc">DropEmpty</span><span class="p">(</span><span class="n">QueryOp</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Remove rows with empty values in some specified columns.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    cols</span>
+<span class="sd">        Columns in which, if a value is empty, the corresponding row is removed.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; DropEmpty(&quot;col1&quot;)(table)</span>
+<span class="sd">    &gt;&gt;&gt; DropEmpty([&quot;col1&quot;, &quot;col2&quot;])(table)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cols</span><span class="p">:</span> <span class="n">typing</span><span class="o">.</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cols</span> <span class="o">=</span> <span class="n">cols</span>
+
+<div class="viewcode-block" id="DropEmpty.__call__">
+<a class="viewcode-back" href="../../../reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">table</span><span class="p">:</span> <span class="n">TableTypes</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Subquery</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Process the table.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        table</span>
+<span class="sd">            Table on which to perform the operation.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        sqlalchemy.sql.selectable.Subquery</span>
+<span class="sd">            Processed table.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cols</span> <span class="o">=</span> <span class="n">to_list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cols</span><span class="p">)</span>
+        <span class="n">table</span> <span class="o">=</span> <span class="n">_process_checks</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cols</span><span class="p">)</span>
+
+        <span class="n">cond</span> <span class="o">=</span> <span class="n">and_</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">not_equals</span><span class="p">(</span><span class="n">get_column</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">col</span><span class="p">),</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cols</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">select</span><span class="p">(</span><span class="n">table</span><span class="p">)</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">cond</span><span class="p">)</span><span class="o">.</span><span class="n">subquery</span><span class="p">()</span></div>
+</div>
+
+
+
 <div class="viewcode-block" id="Apply">
 <a class="viewcode-back" href="../../../reference/api/_autosummary/cyclops.query.ops.Apply.html#cyclops.query.ops.Apply">[docs]</a>
 <span class="k">class</span> <span class="nc">Apply</span><span class="p">(</span><span class="n">QueryOp</span><span class="p">):</span>
diff --git a/api/_sources/reference/api/_autosummary/cyclops.query.ops.DropEmpty.rst.txt b/api/_sources/reference/api/_autosummary/cyclops.query.ops.DropEmpty.rst.txt
new file mode 100644
index 000000000..ecc73fc03
--- /dev/null
+++ b/api/_sources/reference/api/_autosummary/cyclops.query.ops.DropEmpty.rst.txt
@@ -0,0 +1,24 @@
+cyclops.query.ops.DropEmpty
+===========================
+
+.. currentmodule:: cyclops.query.ops
+
+.. autoclass:: DropEmpty
+   :members:
+   :show-inheritance:
+   :inherited-members:
+   :special-members: __call__, __add__, __mul__
+
+   
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+      :nosignatures:
+   
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/api/_sources/reference/api/_autosummary/cyclops.query.ops.rst.txt b/api/_sources/reference/api/_autosummary/cyclops.query.ops.rst.txt
index 0f51ce378..74ae136fd 100644
--- a/api/_sources/reference/api/_autosummary/cyclops.query.ops.rst.txt
+++ b/api/_sources/reference/api/_autosummary/cyclops.query.ops.rst.txt
@@ -42,6 +42,7 @@
       ConditionSubstring
       Distinct
       Drop
+      DropEmpty
       DropNulls
       ExtractTimestampComponent
       FillNull
diff --git a/api/_sources/tutorials/gemini/query_api.ipynb.txt b/api/_sources/tutorials/gemini/query_api.ipynb.txt
index 1a3f4c8b9..ce7c159aa 100644
--- a/api/_sources/tutorials/gemini/query_api.ipynb.txt
+++ b/api/_sources/tutorials/gemini/query_api.ipynb.txt
@@ -26,36 +26,46 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:15,267 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 09:17:45,449 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['information_schema.sql_implementation_info',\n",
-       " 'information_schema.sql_parts',\n",
-       " 'information_schema.sql_sizing',\n",
-       " 'information_schema.sql_features',\n",
-       " 'public.ip_administrative',\n",
+       "['public.lookup_icd10_ca_description',\n",
+       " 'public.lookup_statcan',\n",
+       " 'public.lookup_cci',\n",
+       " 'public.lookup_icd10_ca_to_ccsr',\n",
+       " 'public.lookup_ip_administrative',\n",
+       " 'public.lookup_lab_concept',\n",
+       " 'public.lookup_vitals_concept',\n",
+       " 'public.lookup_pharmacy_concept',\n",
+       " 'public.lookup_diagnosis',\n",
+       " 'public.locality_variables',\n",
+       " 'public.admdad',\n",
        " 'public.derived_variables',\n",
-       " 'public.diagnosis',\n",
-       " 'public.vitals',\n",
-       " 'public.intervention',\n",
-       " 'public.er_administrative',\n",
-       " 'public.imaging',\n",
-       " 'public.pharmacy',\n",
+       " 'public.ipscu',\n",
+       " 'public.lookup_phy_characteristics',\n",
        " 'public.lab',\n",
-       " 'public.room_transfer',\n",
-       " 'public.ip_scu',\n",
-       " 'public.lookup_ip_administrative',\n",
+       " 'public.ipintervention',\n",
        " 'public.lookup_ccsr',\n",
-       " 'public.hospital',\n",
-       " 'public.lookup_er_administrative',\n",
-       " 'public.blood_transfusion',\n",
+       " 'public.lookup_pharmacy_route',\n",
+       " 'public.lookup_transfusion_concept',\n",
        " 'public.lookup_ip_scu',\n",
-       " 'public.lookup_diagnosis',\n",
+       " 'public.lookup_er_administrative',\n",
+       " 'public.lookup_imaging',\n",
+       " 'public.pharmacy',\n",
+       " 'public.radiology',\n",
+       " 'public.lookup_transfer',\n",
+       " 'public.ipdiagnosis',\n",
        " 'public.lookup_room_transfer',\n",
-       " 'public.lookup_imaging']"
+       " 'public.er',\n",
+       " 'public.erdiagnosis',\n",
+       " 'public.erintervention',\n",
+       " 'public.roomtransfer',\n",
+       " 'public.transfusion',\n",
+       " 'public.vitals',\n",
+       " 'public.lookup_hospital_num']"
       ]
      },
      "execution_count": 1,
@@ -72,12 +82,12 @@
     "\n",
     "querier = GEMINIQuerier(\n",
     "    host=\"db.gemini-hpc.ca\",\n",
-    "    database=\"delirium_v3_0_0\",\n",
+    "    database=\"delirium_v4_0_1\",\n",
     "    user=\"username\",\n",
     "    password=\"password\",\n",
     ")\n",
     "# List all tables.\n",
-    "querier.list_tables()"
+    "querier.list_tables(\"public\")"
    ]
   },
   {
@@ -98,15 +108,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:20,365 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:20,366 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 4.825447 s\n"
+      "2023-09-21 09:17:51,902 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:51,903 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 6.093352 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "189606 rows extracted!\n"
+      "189734 rows extracted!\n"
      ]
     }
    ],
@@ -118,8 +128,8 @@
     "    ),\n",
     "    qo.Distinct(\"patient_id_hashed\"),\n",
     ")\n",
-    "encounters_qi = querier.public.ip_administrative(ops=ops)\n",
-    "encounters = encounters_qi.run()\n",
+    "encounters = querier.public.admdad()\n",
+    "encounters = encounters.ops(ops).run()\n",
     "print(f\"{len(encounters)} rows extracted!\")"
    ]
   },
@@ -141,25 +151,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:21,372 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:21,373 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.992901 s\n"
+      "2023-09-21 09:17:52,591 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:52,592 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.675141 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17071 rows extracted!\n"
+      "32567 rows extracted!\n"
      ]
     }
    ],
    "source": [
     "ops = qo.Sequential(\n",
+    "    qo.Cast(\"discharge_date_time\", \"timestamp\"),\n",
     "    qo.ConditionAfterDate(\"discharge_date_time\", \"2015-04-01\"),\n",
     "    qo.ConditionBeforeDate(\"discharge_date_time\", \"2016-03-31\"),\n",
     ")\n",
-    "encounters_qi = querier.get_interface(encounters_qi.query, ops=ops)\n",
-    "encounters = encounters_qi.run()\n",
+    "encounters_query = querier.public.admdad()\n",
+    "encounters_query = encounters_query.ops(ops)\n",
+    "encounters = encounters_query.run()\n",
     "print(f\"{len(encounters)} rows extracted!\")"
    ]
   },
@@ -181,8 +193,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:22,181 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:22,182 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.800708 s\n"
+      "2023-09-21 09:17:52,856 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:52,857 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.145693 s\n"
      ]
     },
     {
@@ -194,9 +206,8 @@
     }
    ],
    "source": [
-    "ops = qo.GroupByAggregate(\"hospital_id\", {\"hospital_id\": (\"count\", \"count\")})\n",
-    "encounters_per_site_qi = querier.get_interface(encounters_qi.query, ops=ops)\n",
-    "encounters_per_site = encounters_per_site_qi.run()\n",
+    "ops = qo.GroupByAggregate(\"hospital_num\", {\"hospital_num\": (\"count\", \"count\")})\n",
+    "encounters_per_site = encounters_query.ops(ops).run()\n",
     "print(f\"{len(encounters_per_site)} rows extracted!\")"
    ]
   },
@@ -205,7 +216,7 @@
    "id": "848c5b90-1e66-4a5a-9ebb-9c0caa105c3f",
    "metadata": {},
    "source": [
-    "## Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?"
+    "## Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?"
    ]
   },
   {
@@ -218,76 +229,37 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:59,972 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:59,973 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 37.740935 s\n"
+      "2023-09-21 09:26:19,814 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:26:19,815 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 506.939296 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 rows extracted!\n"
+      "1 rows extracted!\n",
+      "   hospital_num  count\n",
+      "0           101   9305\n"
      ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hospital_id</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>SMH</td>\n",
-       "      <td>6053</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  hospital_id  count\n",
-       "0         SMH   6053"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "encounter_ops = qo.ConditionEquals(\"hospital_id\", \"SMH\")\n",
-    "encounters_qi = querier.public.ip_administrative(ops=encounter_ops)\n",
+    "encounters = querier.public.admdad()\n",
+    "encounters = encounters.ops(qo.ConditionEquals(\"hospital_num\", 101))\n",
     "lab_ops = qo.Sequential(\n",
-    "    qo.ConditionAfterDate(\"sample_collection_date_time\", \"2015-04-01\"),\n",
-    "    qo.ConditionBeforeDate(\"sample_collection_date_time\", \"2015-05-31\"),\n",
-    "    qo.ConditionSubstring(\"lab_test_name_mapped\", \"sodium\"),\n",
-    "    qo.GroupByAggregate(\"hospital_id\", {\"hospital_id\": (\"count\", \"count\")}),\n",
+    "    qo.DropEmpty(\"collection_date_time\"),\n",
+    "    qo.Cast(\"collection_date_time\", \"timestamp\"),\n",
+    "    qo.ConditionAfterDate(\"collection_date_time\", \"2015-04-01\"),\n",
+    "    qo.ConditionBeforeDate(\"collection_date_time\", \"2015-05-31\"),\n",
+    "    qo.ConditionSubstring(\"test_type_mapped\", \"sodium\"),\n",
     ")\n",
-    "lab_qi = querier.public.lab(\n",
-    "    ops=lab_ops,\n",
-    "    join=qo.JoinArgs(join_table=encounters_qi.query, on=\"genc_id\"),\n",
+    "labs = querier.public.lab()\n",
+    "labs = labs.ops(lab_ops)\n",
+    "encounters_labs = encounters.join(labs, on=\"genc_id\")\n",
+    "encounters_labs = encounters_labs.ops(\n",
+    "    qo.GroupByAggregate(\"hospital_num\", {\"hospital_num\": (\"count\", \"count\")}),\n",
     ")\n",
-    "sodium_tests = lab_qi.run()\n",
+    "sodium_tests = encounters_labs.run()\n",
     "print(f\"{len(sodium_tests)} rows extracted!\")\n",
     "print(sodium_tests)"
    ]
diff --git a/api/api.html b/api/api.html
index bf1581d98..6980902af 100644
--- a/api/api.html
+++ b/api/api.html
@@ -349,6 +349,13 @@ <h1>API Reference<a class="headerlink" href="#api-reference" title="Link to this
 </li>
 </ul>
 </li>
+<li class="toctree-l3"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty"><code class="docutils literal notranslate"><span class="pre">DropEmpty</span></code></a><ul>
+<li class="toctree-l5"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty.__call__"><code class="docutils literal notranslate"><span class="pre">DropEmpty.__call__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls"><code class="docutils literal notranslate"><span class="pre">DropNulls</span></code></a><ul>
 <li class="toctree-l5"><a class="reference internal" href="reference/api/_autosummary/cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls.__call__"><code class="docutils literal notranslate"><span class="pre">DropNulls.__call__()</span></code></a></li>
diff --git a/api/genindex.html b/api/genindex.html
index aa6656e49..052a6cfd8 100644
--- a/api/genindex.html
+++ b/api/genindex.html
@@ -357,6 +357,8 @@ <h2 id="_">_</h2>
         <li><a href="reference/api/_autosummary/cyclops.query.ops.Distinct.html#cyclops.query.ops.Distinct.__call__">(Distinct method)</a>
 </li>
         <li><a href="reference/api/_autosummary/cyclops.query.ops.Drop.html#cyclops.query.ops.Drop.__call__">(Drop method)</a>
+</li>
+        <li><a href="reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty.__call__">(DropEmpty method)</a>
 </li>
         <li><a href="reference/api/_autosummary/cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls.__call__">(DropNulls method)</a>
 </li>
@@ -1482,6 +1484,8 @@ <h2 id="D">D</h2>
       <li><a href="reference/api/_autosummary/cyclops.query.ops.Distinct.html#cyclops.query.ops.Distinct">Distinct (class in cyclops.query.ops)</a>
 </li>
       <li><a href="reference/api/_autosummary/cyclops.query.ops.Drop.html#cyclops.query.ops.Drop">Drop (class in cyclops.query.ops)</a>
+</li>
+      <li><a href="reference/api/_autosummary/cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty">DropEmpty (class in cyclops.query.ops)</a>
 </li>
       <li><a href="reference/api/_autosummary/cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls">DropNulls (class in cyclops.query.ops)</a>
 </li>
diff --git a/api/objects.inv b/api/objects.inv
index f57ebea46..59e6c67fc 100644
Binary files a/api/objects.inv and b/api/objects.inv differ
diff --git a/api/reference/api/_autosummary/cyclops.query.interface.QueryInterface.html b/api/reference/api/_autosummary/cyclops.query.interface.QueryInterface.html
index 5eab2d7ea..59c0c4bd1 100644
--- a/api/reference/api/_autosummary/cyclops.query.interface.QueryInterface.html
+++ b/api/reference/api/_autosummary/cyclops.query.interface.QueryInterface.html
@@ -256,7 +256,7 @@ <h1>cyclops.query.interface.QueryInterface<a class="headerlink" href="#cyclops-q
 
 <dl class="py property">
 <dt class="sig sig-object py" id="cyclops.query.interface.QueryInterface.data">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.0)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.html#dask.dataframe.DataFrame" title="(in Dask)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.9/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a></em><a class="headerlink" href="#cyclops.query.interface.QueryInterface.data" title="Link to this definition"></a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.html#dask.dataframe.DataFrame" title="(in Dask)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.9/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a></em><a class="headerlink" href="#cyclops.query.interface.QueryInterface.data" title="Link to this definition"></a></dt>
 <dd><p>Get data.</p>
 </dd></dl>
 
@@ -320,7 +320,7 @@ <h1>cyclops.query.interface.QueryInterface<a class="headerlink" href="#cyclops-q
 <dd class="field-even"><p>Query result.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.0)">pandas.DataFrame</a> or dask.DataFrame or datasets.Dataset</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">pandas.DataFrame</a> or dask.DataFrame or datasets.Dataset</p>
 </dd>
 </dl>
 </dd></dl>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.AddColumn.html b/api/reference/api/_autosummary/cyclops.query.ops.AddColumn.html
index 891bad54f..e9a458a5b 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.AddColumn.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.AddColumn.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaColumn.html b/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaColumn.html
index c8677447b..8063e8ba1 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaColumn.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaColumn.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaConstant.html b/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaConstant.html
index f8102d98d..26b1536f3 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaConstant.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.AddDeltaConstant.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.AddNumeric.html b/api/reference/api/_autosummary/cyclops.query.ops.AddNumeric.html
index 431d7b190..a97bd6f31 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.AddNumeric.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.AddNumeric.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.And.html b/api/reference/api/_autosummary/cyclops.query.ops.And.html
index f4f4dd79e..f1755736a 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.And.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.And.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Apply.html b/api/reference/api/_autosummary/cyclops.query.ops.Apply.html
index 0a595abe8..76ab3d389 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Apply.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Apply.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Cast.html b/api/reference/api/_autosummary/cyclops.query.ops.Cast.html
index 1b460d535..1eafb7470 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Cast.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Cast.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionAfterDate.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionAfterDate.html
index 80c0ad295..5a6753a6d 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionAfterDate.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionAfterDate.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionBeforeDate.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionBeforeDate.html
index 725fc2792..87dc701ab 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionBeforeDate.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionBeforeDate.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionEndsWith.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionEndsWith.html
index 8fe678201..b11e4ddbe 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionEndsWith.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionEndsWith.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionEquals.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionEquals.html
index 5fcd81011..7025ae14c 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionEquals.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionEquals.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionGreaterThan.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionGreaterThan.html
index 30db85b61..b7d91a543 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionGreaterThan.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionGreaterThan.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionIn.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionIn.html
index 5826b64f2..7249ba01f 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionIn.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionIn.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionInMonths.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionInMonths.html
index a4d976850..a7b230085 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionInMonths.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionInMonths.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionInYears.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionInYears.html
index 6600d02cd..6347e2e3a 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionInYears.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionInYears.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionLessThan.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionLessThan.html
index b27410f34..506906afc 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionLessThan.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionLessThan.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionLike.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionLike.html
index 7676b39c5..89ee65bd3 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionLike.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionLike.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionRegexMatch.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionRegexMatch.html
index 8410deda0..fef6a6f95 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionRegexMatch.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionRegexMatch.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionStartsWith.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionStartsWith.html
index cba31c6e1..8844817c0 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionStartsWith.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionStartsWith.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ConditionSubstring.html b/api/reference/api/_autosummary/cyclops.query.ops.ConditionSubstring.html
index 8986722cc..b5c7d9434 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ConditionSubstring.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ConditionSubstring.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4 current"><a class="current reference internal" href="#">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Distinct.html b/api/reference/api/_autosummary/cyclops.query.ops.Distinct.html
index 097dc6f1a..950a094ac 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Distinct.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Distinct.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4 current"><a class="current reference internal" href="#">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Drop.html b/api/reference/api/_autosummary/cyclops.query.ops.Drop.html
index 875dee1ca..a25726da6 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Drop.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Drop.html
@@ -30,7 +30,7 @@
     <script src="../../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="cyclops.query.ops.DropNulls" href="cyclops.query.ops.DropNulls.html" />
+    <link rel="next" title="cyclops.query.ops.DropEmpty" href="cyclops.query.ops.DropEmpty.html" />
     <link rel="prev" title="cyclops.query.ops.Distinct" href="cyclops.query.ops.Distinct.html" /> 
 </head>
 
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4 current"><a class="current reference internal" href="#">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
@@ -274,7 +275,7 @@ <h1>cyclops.query.ops.Drop<a class="headerlink" href="#cyclops-query-ops-drop" t
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="cyclops.query.ops.Distinct.html" class="btn btn-neutral float-left" title="cyclops.query.ops.Distinct" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="cyclops.query.ops.DropNulls.html" class="btn btn-neutral float-right" title="cyclops.query.ops.DropNulls" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="cyclops.query.ops.DropEmpty.html" class="btn btn-neutral float-right" title="cyclops.query.ops.DropEmpty" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.DropEmpty.html b/api/reference/api/_autosummary/cyclops.query.ops.DropEmpty.html
new file mode 100644
index 000000000..a0fa1483f
--- /dev/null
+++ b/api/reference/api/_autosummary/cyclops.query.ops.DropEmpty.html
@@ -0,0 +1,335 @@
+
+
+
+
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>cyclops.query.ops.DropEmpty &mdash; cyclops  documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=80d5e7a1" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/css/cyclops.css?v=fc0e58c9" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
+  <!--[if lt IE 9]>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../../_static/documentation_options.js?v=5929fcd5"></script>
+        <script src="../../../_static/doctools.js?v=888ff710"></script>
+        <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <link rel="next" title="cyclops.query.ops.DropNulls" href="cyclops.query.ops.DropNulls.html" />
+    <link rel="prev" title="cyclops.query.ops.Drop" href="cyclops.query.ops.Drop.html" /> 
+</head>
+
+<body class="wy-body-for-nav">
+  <!--
+    Lie about what theme we really are when some RTD-injected JS fetches the
+    full versions panel from the RTD API.  RTD's JS replaces the stub versions
+    panel in the Sphinx-generated HTML with the full versions panel it fetches
+    from the RTD API.  If the API thinks we're not sphinx_rtd_theme, it will
+    serve us the wrong HTML and the panel will float as a "badge" when it
+    shouldn't.  As a customized version of sphinx_rtd_theme, we really do want
+    the same HTML it gets.  See also the diagnoses in
+    <https://github.com/nextstrain/docs.nextstrain.org/issues/76>.
+
+    This bit of JS finds the data RTD injects into the page and modifies it
+    before the code that RTD injects runs and looks at the data.  RTD's
+    <script>s (in <head>) run before this JS, but they wait until the DOM is
+    ready to actually do any work.  This gives us a chance to modify the data
+    during DOM load before the RTD code actually uses it.
+
+      -trs, 27 Jan 2022
+  -->
+  <script>
+    (() => {
+      try {
+        console.log("Lying about the theme to RTD's JS so the versions panel works properly. 🙈");
+
+        /* Update global variable, which is used in the request to get the RTD
+         * "footer" that includes the versions panel.
+         */
+        if (window.READTHEDOCS_DATA)
+          window.READTHEDOCS_DATA.theme = "sphinx_rtd_theme";
+
+        /* Update stored JSON in case anything else deserializes it later.
+         * Comments in the RTD-injected HTML source claim the global variable
+         * above is deprecated.
+         */
+        var script = document.querySelector("#READTHEDOCS_DATA");
+        if (script)
+          script.innerHTML = JSON.stringify({ ...JSON.parse(script.innerHTML), theme: "sphinx_rtd_theme" });
+      }
+      catch (err) {
+        console.log("Lying about the theme to RTD's JS failed… oh well. 🤷", err);
+      }
+    })();
+  </script>
+
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #efeeed" >
+
+
+
+
+  <a href="https://vectorinstitute.github.io/cyclops/">
+    <img src="../../../_static/cyclops_logo-dark.png" class="logo" alt="Logo"/>
+  </a>
+
+
+
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+  <section id="cyclops-query-ops-dropempty">
+<h1>cyclops.query.ops.DropEmpty<a class="headerlink" href="#cyclops-query-ops-dropempty" title="Link to this heading"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="cyclops.query.ops.DropEmpty">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">DropEmpty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cols</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/cyclops/query/ops.html#DropEmpty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cyclops.query.ops.DropEmpty" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="cyclops.query.ops.QueryOp.html#cyclops.query.ops.QueryOp" title="cyclops.query.ops.QueryOp"><code class="xref py py-class docutils literal notranslate"><span class="pre">QueryOp</span></code></a></p>
+<p>Remove rows with empty values in some specified columns.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>cols</strong> (<a class="reference external" href="https://docs.python.org/3.9/library/typing.html#typing.Union" title="(in Python v3.9)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code></a>[<a class="reference external" href="https://docs.python.org/3.9/library/stdtypes.html#str" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3.9/library/typing.html#typing.List" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">List</span></code></a>[<a class="reference external" href="https://docs.python.org/3.9/library/stdtypes.html#str" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>]]) – Columns in which, if a value is empty, the corresponding row is removed.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">DropEmpty</span><span class="p">(</span><span class="s2">&quot;col1&quot;</span><span class="p">)(</span><span class="n">table</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">DropEmpty</span><span class="p">([</span><span class="s2">&quot;col1&quot;</span><span class="p">,</span> <span class="s2">&quot;col2&quot;</span><span class="p">])(</span><span class="n">table</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="cyclops.query.ops.DropEmpty.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">table</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/cyclops/query/ops.html#DropEmpty.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cyclops.query.ops.DropEmpty.__call__" title="Link to this definition"></a></dt>
+<dd><p>Process the table.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>table</strong> (<a class="reference external" href="https://docs.python.org/3.9/library/typing.html#typing.Union" title="(in Python v3.9)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code></a>[<a class="reference external" href="https://docs.sqlalchemy.org/en/20/core/selectable.html#sqlalchemy.sql.expression.Select" title="(in SQLAlchemy v2.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Select</span></code></a>, <a class="reference external" href="https://docs.sqlalchemy.org/en/20/core/selectable.html#sqlalchemy.sql.expression.Subquery" title="(in SQLAlchemy v2.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Subquery</span></code></a>, <a class="reference external" href="https://docs.sqlalchemy.org/en/20/core/metadata.html#sqlalchemy.schema.Table" title="(in SQLAlchemy v2.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Table</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">DBTable</span></code>]) – Table on which to perform the operation.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Processed table.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.sqlalchemy.org/en/20/core/selectable.html#sqlalchemy.sql.expression.Subquery" title="(in SQLAlchemy v2.0)">sqlalchemy.sql.selectable.Subquery</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+        <a href="cyclops.query.ops.DropNulls.html" class="btn btn-neutral float-right" title="cyclops.query.ops.DropNulls" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+
+  <div class="footer-small-text">
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diff --git a/api/reference/api/_autosummary/cyclops.query.ops.DropNulls.html b/api/reference/api/_autosummary/cyclops.query.ops.DropNulls.html
index 6cbae87b9..b98613625 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.DropNulls.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.DropNulls.html
@@ -31,7 +31,7 @@
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="cyclops.query.ops.ExtractTimestampComponent" href="cyclops.query.ops.ExtractTimestampComponent.html" />
-    <link rel="prev" title="cyclops.query.ops.Drop" href="cyclops.query.ops.Drop.html" /> 
+    <link rel="prev" title="cyclops.query.ops.DropEmpty" href="cyclops.query.ops.DropEmpty.html" /> 
 </head>
 
 <body class="wy-body-for-nav">
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4 current"><a class="current reference internal" href="#">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
@@ -273,7 +274,7 @@ <h1>cyclops.query.ops.DropNulls<a class="headerlink" href="#cyclops-query-ops-dr
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="cyclops.query.ops.Drop.html" class="btn btn-neutral float-left" title="cyclops.query.ops.Drop" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="cyclops.query.ops.DropEmpty.html" class="btn btn-neutral float-left" title="cyclops.query.ops.DropEmpty" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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     </div>
 
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ExtractTimestampComponent.html b/api/reference/api/_autosummary/cyclops.query.ops.ExtractTimestampComponent.html
index f70056790..0855e6c64 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ExtractTimestampComponent.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ExtractTimestampComponent.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4 current"><a class="current reference internal" href="#">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.FillNull.html b/api/reference/api/_autosummary/cyclops.query.ops.FillNull.html
index b56e2d1f2..80aafff59 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.FillNull.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.FillNull.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
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diff --git a/api/reference/api/_autosummary/cyclops.query.ops.GroupByAggregate.html b/api/reference/api/_autosummary/cyclops.query.ops.GroupByAggregate.html
index 773e6ee01..f5b20e9da 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.GroupByAggregate.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.GroupByAggregate.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
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diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Join.html b/api/reference/api/_autosummary/cyclops.query.ops.Join.html
index 5a785ecb8..70c8bdcfc 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Join.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Join.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
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diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Keep.html b/api/reference/api/_autosummary/cyclops.query.ops.Keep.html
index 167e84951..58b184074 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Keep.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Keep.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Limit.html b/api/reference/api/_autosummary/cyclops.query.ops.Limit.html
index 5cdec38f5..a3501d532 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Limit.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Limit.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
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diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Literal.html b/api/reference/api/_autosummary/cyclops.query.ops.Literal.html
index af3387bb5..b781307ee 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Literal.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Literal.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Or.html b/api/reference/api/_autosummary/cyclops.query.ops.Or.html
index 6b39eab0a..f9bac63c1 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Or.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Or.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.OrderBy.html b/api/reference/api/_autosummary/cyclops.query.ops.OrderBy.html
index df12dd979..0c2f2523d 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.OrderBy.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.OrderBy.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.QueryOp.html b/api/reference/api/_autosummary/cyclops.query.ops.QueryOp.html
index 869565ae7..37544c277 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.QueryOp.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.QueryOp.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.RandomizeOrder.html b/api/reference/api/_autosummary/cyclops.query.ops.RandomizeOrder.html
index 4cdbac031..8289df860 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.RandomizeOrder.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.RandomizeOrder.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Rename.html b/api/reference/api/_autosummary/cyclops.query.ops.Rename.html
index 7053eb893..dbb44f134 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Rename.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Rename.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Reorder.html b/api/reference/api/_autosummary/cyclops.query.ops.Reorder.html
index 76d44222d..7da95498d 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Reorder.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Reorder.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.ReorderAfter.html b/api/reference/api/_autosummary/cyclops.query.ops.ReorderAfter.html
index 0b89aeebf..726dba8c1 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.ReorderAfter.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.ReorderAfter.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Sequential.html b/api/reference/api/_autosummary/cyclops.query.ops.Sequential.html
index ff878995b..e58c37c5e 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Sequential.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Sequential.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Substring.html b/api/reference/api/_autosummary/cyclops.query.ops.Substring.html
index 7c509bd07..d945c7a36 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Substring.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Substring.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Trim.html b/api/reference/api/_autosummary/cyclops.query.ops.Trim.html
index 238f5e6de..62fe31bf0 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Trim.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Trim.html
@@ -151,6 +151,7 @@
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ConditionSubstring.html">cyclops.query.ops.ConditionSubstring</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Distinct.html">cyclops.query.ops.Distinct</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.Union.html b/api/reference/api/_autosummary/cyclops.query.ops.Union.html
index fa808a2e4..ab8233e70 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.Union.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.Union.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
diff --git a/api/reference/api/_autosummary/cyclops.query.ops.html b/api/reference/api/_autosummary/cyclops.query.ops.html
index c0aeafe73..ee8502b62 100644
--- a/api/reference/api/_autosummary/cyclops.query.ops.html
+++ b/api/reference/api/_autosummary/cyclops.query.ops.html
@@ -151,6 +151,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.Drop.html">cyclops.query.ops.Drop</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropEmpty.html">cyclops.query.ops.DropEmpty</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.DropNulls.html">cyclops.query.ops.DropNulls</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html">cyclops.query.ops.ExtractTimestampComponent</a></li>
 <li class="toctree-l4"><a class="reference internal" href="cyclops.query.ops.FillNull.html">cyclops.query.ops.FillNull</a></li>
@@ -296,61 +297,64 @@
 <tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Drop.html#cyclops.query.ops.Drop" title="cyclops.query.ops.Drop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Drop</span></code></a></p></td>
 <td><p>Drop some columns.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls" title="cyclops.query.ops.DropNulls"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropNulls</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.DropEmpty.html#cyclops.query.ops.DropEmpty" title="cyclops.query.ops.DropEmpty"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropEmpty</span></code></a></p></td>
+<td><p>Remove rows with empty values in some specified columns.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.DropNulls.html#cyclops.query.ops.DropNulls" title="cyclops.query.ops.DropNulls"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropNulls</span></code></a></p></td>
 <td><p>Remove rows with null values in some specified columns.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html#cyclops.query.ops.ExtractTimestampComponent" title="cyclops.query.ops.ExtractTimestampComponent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExtractTimestampComponent</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.ExtractTimestampComponent.html#cyclops.query.ops.ExtractTimestampComponent" title="cyclops.query.ops.ExtractTimestampComponent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExtractTimestampComponent</span></code></a></p></td>
 <td><p>Extract a component such as year or month from a timestamp column.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.FillNull.html#cyclops.query.ops.FillNull" title="cyclops.query.ops.FillNull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FillNull</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.FillNull.html#cyclops.query.ops.FillNull" title="cyclops.query.ops.FillNull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FillNull</span></code></a></p></td>
 <td><p>Fill NULL values with a given value.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.GroupByAggregate.html#cyclops.query.ops.GroupByAggregate" title="cyclops.query.ops.GroupByAggregate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupByAggregate</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.GroupByAggregate.html#cyclops.query.ops.GroupByAggregate" title="cyclops.query.ops.GroupByAggregate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupByAggregate</span></code></a></p></td>
 <td><p>Aggregate over a group by object.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Join.html#cyclops.query.ops.Join" title="cyclops.query.ops.Join"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Join</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Join.html#cyclops.query.ops.Join" title="cyclops.query.ops.Join"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Join</span></code></a></p></td>
 <td><p>Join a table with another table.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Keep.html#cyclops.query.ops.Keep" title="cyclops.query.ops.Keep"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Keep</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Keep.html#cyclops.query.ops.Keep" title="cyclops.query.ops.Keep"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Keep</span></code></a></p></td>
 <td><p>Keep only the specified columns in a table.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Limit.html#cyclops.query.ops.Limit" title="cyclops.query.ops.Limit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Limit</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Limit.html#cyclops.query.ops.Limit" title="cyclops.query.ops.Limit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Limit</span></code></a></p></td>
 <td><p>Limit the number of rows returned in a query.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Literal.html#cyclops.query.ops.Literal" title="cyclops.query.ops.Literal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Literal</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Literal.html#cyclops.query.ops.Literal" title="cyclops.query.ops.Literal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Literal</span></code></a></p></td>
 <td><p>Add a literal column to a table.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Or.html#cyclops.query.ops.Or" title="cyclops.query.ops.Or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Or</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Or.html#cyclops.query.ops.Or" title="cyclops.query.ops.Or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Or</span></code></a></p></td>
 <td><p>Combine multiple condition query ops using an OR.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.OrderBy.html#cyclops.query.ops.OrderBy" title="cyclops.query.ops.OrderBy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrderBy</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.OrderBy.html#cyclops.query.ops.OrderBy" title="cyclops.query.ops.OrderBy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrderBy</span></code></a></p></td>
 <td><p>Order, or sort, the rows of a table by some columns.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.QueryOp.html#cyclops.query.ops.QueryOp" title="cyclops.query.ops.QueryOp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QueryOp</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.QueryOp.html#cyclops.query.ops.QueryOp" title="cyclops.query.ops.QueryOp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QueryOp</span></code></a></p></td>
 <td><p>Base class for query operations.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.RandomizeOrder.html#cyclops.query.ops.RandomizeOrder" title="cyclops.query.ops.RandomizeOrder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomizeOrder</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.RandomizeOrder.html#cyclops.query.ops.RandomizeOrder" title="cyclops.query.ops.RandomizeOrder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomizeOrder</span></code></a></p></td>
 <td><p>Randomize order of table rows.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Rename.html#cyclops.query.ops.Rename" title="cyclops.query.ops.Rename"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rename</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Rename.html#cyclops.query.ops.Rename" title="cyclops.query.ops.Rename"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rename</span></code></a></p></td>
 <td><p>Rename some columns.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Reorder.html#cyclops.query.ops.Reorder" title="cyclops.query.ops.Reorder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Reorder</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Reorder.html#cyclops.query.ops.Reorder" title="cyclops.query.ops.Reorder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Reorder</span></code></a></p></td>
 <td><p>Reorder the columns in a table.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.ReorderAfter.html#cyclops.query.ops.ReorderAfter" title="cyclops.query.ops.ReorderAfter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReorderAfter</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.ReorderAfter.html#cyclops.query.ops.ReorderAfter" title="cyclops.query.ops.ReorderAfter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReorderAfter</span></code></a></p></td>
 <td><p>Reorder a number of columns to come after a specified column.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Sequential.html#cyclops.query.ops.Sequential" title="cyclops.query.ops.Sequential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sequential</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Sequential.html#cyclops.query.ops.Sequential" title="cyclops.query.ops.Sequential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sequential</span></code></a></p></td>
 <td><p>Sequential query operations class.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Substring.html#cyclops.query.ops.Substring" title="cyclops.query.ops.Substring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Substring</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Substring.html#cyclops.query.ops.Substring" title="cyclops.query.ops.Substring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Substring</span></code></a></p></td>
 <td><p>Get substring of a string column.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Trim.html#cyclops.query.ops.Trim" title="cyclops.query.ops.Trim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Trim</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Trim.html#cyclops.query.ops.Trim" title="cyclops.query.ops.Trim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Trim</span></code></a></p></td>
 <td><p>Trim the whitespace from some string columns.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="cyclops.query.ops.Union.html#cyclops.query.ops.Union" title="cyclops.query.ops.Union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Union</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="cyclops.query.ops.Union.html#cyclops.query.ops.Union" title="cyclops.query.ops.Union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Union</span></code></a></p></td>
 <td><p>Union two tables.</p></td>
 </tr>
 </tbody>
diff --git a/api/searchindex.js b/api/searchindex.js
index ac1ddc671..a4db6dfd9 100644
--- a/api/searchindex.js
+++ b/api/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "contributing", "index", "intro", "reference/api/_autosummary/cyclops.data.features.medical_image", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage", "reference/api/_autosummary/cyclops.data.slicer", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec", "reference/api/_autosummary/cyclops.data.slicer.compound_filter", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null", "reference/api/_autosummary/cyclops.data.slicer.filter_range", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains", "reference/api/_autosummary/cyclops.data.slicer.filter_value", "reference/api/_autosummary/cyclops.data.slicer.is_datetime", "reference/api/_autosummary/cyclops.data.slicer.overall", "reference/api/_autosummary/cyclops.evaluate.evaluator", "reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate", "reference/api/_autosummary/cyclops.evaluate.fairness.config", "reference/api/_autosummary/cyclops.evaluate.fairness.config.FairnessConfig", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.Accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.BinaryAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MulticlassAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MultilabelAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.AUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.BinaryAUROC", 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"cyclops.evaluate.metrics.stat_scores": [[106, "module-cyclops.evaluate.metrics.stat_scores"]], "cyclops.evaluate.metrics.stat_scores.BinaryStatScores": [[107, "cyclops-evaluate-metrics-stat-scores-binarystatscores"]], "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores": [[108, "cyclops-evaluate-metrics-stat-scores-multiclassstatscores"]], "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores": [[109, "cyclops-evaluate-metrics-stat-scores-multilabelstatscores"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, "cyclops-evaluate-metrics-stat-scores-statscores"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[115, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[116, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[117, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[118, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[119, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.query.base": [[120, "module-cyclops.query.base"]], "cyclops.query.base.DatasetQuerier": [[121, "cyclops-query-base-datasetquerier"]], "cyclops.query.eicu": [[122, "module-cyclops.query.eicu"]], "cyclops.query.eicu.EICUQuerier": [[123, "cyclops-query-eicu-eicuquerier"]], "cyclops.query.gemini": [[124, "module-cyclops.query.gemini"]], "cyclops.query.gemini.GEMINIQuerier": [[125, "cyclops-query-gemini-geminiquerier"]], "cyclops.query.interface": [[126, "module-cyclops.query.interface"]], "cyclops.query.interface.QueryInterface": [[127, "cyclops-query-interface-queryinterface"]], "cyclops.query.mimiciii": [[128, "module-cyclops.query.mimiciii"]], "cyclops.query.mimiciii.MIMICIIIQuerier": [[129, "cyclops-query-mimiciii-mimiciiiquerier"]], "cyclops.query.mimiciv": [[130, "module-cyclops.query.mimiciv"]], "cyclops.query.mimiciv.MIMICIVQuerier": [[131, "cyclops-query-mimiciv-mimicivquerier"]], "cyclops.query.omop": [[132, "module-cyclops.query.omop"]], "cyclops.query.omop.OMOPQuerier": [[133, "cyclops-query-omop-omopquerier"]], "cyclops.query.ops": [[134, "module-cyclops.query.ops"]], "cyclops.query.ops.AddColumn": [[135, "cyclops-query-ops-addcolumn"]], "cyclops.query.ops.AddDeltaColumn": [[136, "cyclops-query-ops-adddeltacolumn"]], "cyclops.query.ops.AddDeltaConstant": [[137, "cyclops-query-ops-adddeltaconstant"]], "cyclops.query.ops.AddNumeric": [[138, "cyclops-query-ops-addnumeric"]], "cyclops.query.ops.And": [[139, "cyclops-query-ops-and"]], "cyclops.query.ops.Apply": [[140, "cyclops-query-ops-apply"]], "cyclops.query.ops.Cast": [[141, "cyclops-query-ops-cast"]], "cyclops.query.ops.ConditionAfterDate": [[142, "cyclops-query-ops-conditionafterdate"]], "cyclops.query.ops.ConditionBeforeDate": [[143, "cyclops-query-ops-conditionbeforedate"]], "cyclops.query.ops.ConditionEndsWith": [[144, "cyclops-query-ops-conditionendswith"]], "cyclops.query.ops.ConditionEquals": [[145, "cyclops-query-ops-conditionequals"]], "cyclops.query.ops.ConditionGreaterThan": [[146, "cyclops-query-ops-conditiongreaterthan"]], "cyclops.query.ops.ConditionIn": [[147, "cyclops-query-ops-conditionin"]], "cyclops.query.ops.ConditionInMonths": [[148, "cyclops-query-ops-conditioninmonths"]], "cyclops.query.ops.ConditionInYears": [[149, "cyclops-query-ops-conditioninyears"]], "cyclops.query.ops.ConditionLessThan": [[150, "cyclops-query-ops-conditionlessthan"]], "cyclops.query.ops.ConditionLike": [[151, "cyclops-query-ops-conditionlike"]], "cyclops.query.ops.ConditionRegexMatch": [[152, "cyclops-query-ops-conditionregexmatch"]], "cyclops.query.ops.ConditionStartsWith": [[153, "cyclops-query-ops-conditionstartswith"]], "cyclops.query.ops.ConditionSubstring": [[154, "cyclops-query-ops-conditionsubstring"]], "cyclops.query.ops.Distinct": [[155, "cyclops-query-ops-distinct"]], "cyclops.query.ops.Drop": [[156, "cyclops-query-ops-drop"]], "cyclops.query.ops.DropNulls": [[157, "cyclops-query-ops-dropnulls"]], "cyclops.query.ops.ExtractTimestampComponent": [[158, "cyclops-query-ops-extracttimestampcomponent"]], "cyclops.query.ops.FillNull": [[159, "cyclops-query-ops-fillnull"]], "cyclops.query.ops.GroupByAggregate": [[160, "cyclops-query-ops-groupbyaggregate"]], "cyclops.query.ops.Join": [[161, "cyclops-query-ops-join"]], "cyclops.query.ops.Keep": [[162, "cyclops-query-ops-keep"]], "cyclops.query.ops.Limit": [[163, "cyclops-query-ops-limit"]], "cyclops.query.ops.Literal": [[164, "cyclops-query-ops-literal"]], "cyclops.query.ops.Or": [[165, "cyclops-query-ops-or"]], "cyclops.query.ops.OrderBy": [[166, "cyclops-query-ops-orderby"]], "cyclops.query.ops.QueryOp": [[167, "cyclops-query-ops-queryop"]], "cyclops.query.ops.RandomizeOrder": [[168, "cyclops-query-ops-randomizeorder"]], "cyclops.query.ops.Rename": [[169, "cyclops-query-ops-rename"]], "cyclops.query.ops.Reorder": [[170, "cyclops-query-ops-reorder"]], "cyclops.query.ops.ReorderAfter": [[171, "cyclops-query-ops-reorderafter"]], "cyclops.query.ops.Sequential": [[172, "cyclops-query-ops-sequential"]], "cyclops.query.ops.Substring": [[173, "cyclops-query-ops-substring"]], "cyclops.query.ops.Trim": [[174, "cyclops-query-ops-trim"]], "cyclops.query.ops.Union": [[175, "cyclops-query-ops-union"]], "cyclops.report.report": [[176, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[177, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[178, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[179, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[180, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[181, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[182, "module-cyclops.data"]], "cyclops.data.features": [[182, "module-cyclops.data.features"]], "cyclops.evaluate": [[183, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[183, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[183, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[183, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[184, "module-cyclops.monitor"]], "cyclops.query": [[185, "module-cyclops.query"]], "dataset APIs": [[185, "dataset-apis"]], "cyclops.report": [[186, "module-cyclops.report"]], "cyclops.tasks": [[187, "module-cyclops.tasks"]], "Tutorials": [[188, "tutorials"]], "eICU-CRD query API tutorial": [[189, "eICU-CRD-query-API-tutorial"]], "Imports and instantiate EICUQuerier": [[189, "Imports-and-instantiate-EICUQuerier"]], "Example 1. Get all female patients discharged in 2014 (limit to 100 rows).": [[189, "Example-1.-Get-all-female-patients-discharged-in-2014-(limit-to-100-rows)."]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in diagnosisstring), discharged in the year 2015.": [[189, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-diagnosisstring),-discharged-in-the-year-2015."]], "Example 3. Get potassium lab tests for patients discharged in the year 2014, for all teaching hospitals.": [[189, "Example-3.-Get-potassium-lab-tests-for-patients-discharged-in-the-year-2014,-for-all-teaching-hospitals."]], "Example 4. Get glucose medications (substring search) for female patients discharged in 2014.": [[189, "Example-4.-Get-glucose-medications-(substring-search)-for-female-patients-discharged-in-2014."]], "GEMINI query API tutorial": [[190, "GEMINI-query-API-tutorial"]], "Imports and instantiate GEMINIQuerier.": [[190, "Imports-and-instantiate-GEMINIQuerier."]], "Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by patient_id_hashed and discharge_date_time, and then keep the recent record.": [[190, "Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record."]], "Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.": [[190, "Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016."]], "Example 1c. From the above set of encounters, get the total number of admissions for each hospital.": [[190, "Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital."]], "Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?": [[190, "Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?"]], "Heart Failure Prediction": [[191, "Heart-Failure-Prediction"]], "Import Libraries": [[191, "Import-Libraries"], [194, "Import-Libraries"], [197, "Import-Libraries"]], "Constants": [[191, "Constants"], [197, "Constants"]], "Data Loading": [[191, "Data-Loading"]], "Sex values": [[191, "Sex-values"]], "Age distribution": [[191, "Age-distribution"], [197, "Age-distribution"]], "Outcome distribution": [[191, "Outcome-distribution"], [197, "Outcome-distribution"]], "Identifying feature types": [[191, "Identifying-feature-types"], [197, "Identifying-feature-types"]], "Creating data preprocessors": [[191, "Creating-data-preprocessors"], [197, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[191, "Creating-Hugging-Face-Dataset"], [197, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[191, "Model-Creation"], [197, "Model-Creation"]], "Task Creation": [[191, "Task-Creation"], [197, "Task-Creation"]], "Training": [[191, "Training"], [197, "Training"]], "Prediction": [[191, "Prediction"], [197, "Prediction"]], "Evaluation": [[191, "Evaluation"], [197, "Evaluation"]], "Performance over time": [[191, "Performance-over-time"], [194, "Performance-over-time"], [197, "Performance-over-time"]], "Report Generation": [[191, "Report-Generation"], [197, "Report-Generation"]], "MIMIC-III query API tutorial": [[192, "MIMIC-III-query-API-tutorial"]], "Imports and instantiate MIMICIIIQuerier": [[192, "Imports-and-instantiate-MIMICIIIQuerier"]], "Example 1. Get all male patients with a mortality outcome.": [[192, "Example-1.-Get-all-male-patients-with-a-mortality-outcome."]], "Example 2. Get all female patient encounters with diagnoses (gastroenteritis in ICD-9 long title).": [[192, "Example-2.-Get-all-female-patient-encounters-with-diagnoses-(gastroenteritis-in-ICD-9-long-title)."]], "Example 3. Get potassium lab tests for female patients.": [[192, "Example-3.-Get-potassium-lab-tests-for-female-patients."]], "Example 4. Get AaDO2 carevue chart events for male patients that have a valuenum of less than 20.": [[192, "Example-4.-Get-AaDO2-carevue-chart-events-for-male-patients-that-have-a-valuenum-of-less-than-20."]], "MIMIC-IV query API tutorial": [[193, "MIMIC-IV-query-API-tutorial"]], "Imports and instantiate MIMICIVQuerier": [[193, "Imports-and-instantiate-MIMICIVQuerier"]], "Example 1. Get all patient admissions from 2021 or later (approx year of admission)": [[193, "Example-1.-Get-all-patient-admissions-from-2021-or-later-(approx-year-of-admission)"]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in ICD-10 long title), in the year 2015.": [[193, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-ICD-10-long-title),-in-the-year-2015."]], "Example 3. Advanced - uses ConditionRegexMatch from cyclops.query.ops. Get all patient encounters with diagnoses (ICD-9 long title contains schizophrenia and chronic ), in the year 2015.": [[193, "Example-3.-Advanced---uses-ConditionRegexMatch-from-cyclops.query.ops.-Get-all-patient-encounters-with-diagnoses-(ICD-9-long-title-contains-schizophrenia-and-chronic-),-in-the-year-2015."]], "Example 4. Get routine vital signs for patients from year 2015.": [[193, "Example-4.-Get-routine-vital-signs-for-patients-from-year-2015."]], "Example 5. Get hemoglobin lab tests for patients from year 2009.": [[193, "Example-5.-Get-hemoglobin-lab-tests-for-patients-from-year-2009."]], "Example 6. Get radiology reports and filter on keywords lymphadenopathy and infectious occurring together from year 2009.": [[193, "Example-6.-Get-radiology-reports-and-filter-on-keywords-lymphadenopathy-and-infectious-occurring-together-from-year-2009."]], "Example 7. Get all female patient encounters from year 2015, and return as dask dataframe (lazy evaluation) with 4 partitions (batches) aggregated based on subject_id.": [[193, "Example-7.-Get-all-female-patient-encounters-from-year-2015,-and-return-as-dask-dataframe-(lazy-evaluation)-with-4-partitions-(batches)-aggregated-based-on-subject_id."]], "Example 8. Running a raw SQL string.": [[193, "Example-8.-Running-a-raw-SQL-string."]], "Chest X-Ray Disease Classification": [[194, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[194, "Load-Dataset"]], "Load Model and get Predictions": [[194, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[194, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[194, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[194, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[194, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[194, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[194, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[195, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[195, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[195, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[195, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[195, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[195, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[195, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "OMOP query API tutorial": [[196, "OMOP-query-API-tutorial"]], "Imports and instantiate OMOPQuerier.": [[196, "Imports-and-instantiate-OMOPQuerier."], [196, "id1"]], "Example 1. Get all patient visits in or after 2010.": [[196, "Example-1.-Get-all-patient-visits-in-or-after-2010."]], "Example 2. Get measurements for all visits in or after 2020.": [[196, "Example-2.-Get-measurements-for-all-visits-in-or-after-2020."]], "Example 1. Get all patient visits that ended in a mortality outcome in or after 2010.": [[196, "Example-1.-Get-all-patient-visits-that-ended-in-a-mortality-outcome-in-or-after-2010."]], "Example 2. Get all measurements for female patient visits with sepsis diagnoses, that ended in a mortality outcome.": [[196, "Example-2.-Get-all-measurements-for-female-patient-visits-with-sepsis-diagnoses,-that-ended-in-a-mortality-outcome."]], "Prolonged Length of Stay Prediction": [[197, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[197, "Data-Querying"]], "Compute length of stay (labels)": [[197, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[197, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[197, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[197, "Length-of-stay-distribution"]], "Gender distribution": [[197, "Gender-distribution"]], "monitor API": [[198, "monitor-api"]], "query API": [[199, "query-api"]], "Example use cases": [[200, "example-use-cases"]], "Tabular data": [[200, "tabular-data"]], "Kaggle Heart Failure Prediction": [[200, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[200, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[200, "image-data"]], "NIH Chest X-ray classification": [[200, "nih-chest-x-ray-classification"]]}, "indexentries": {"cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "module": [[4, "module-cyclops.data.features.medical_image"], [6, "module-cyclops.data.slicer"], [16, "module-cyclops.evaluate.evaluator"], [18, "module-cyclops.evaluate.fairness.config"], [20, "module-cyclops.evaluate.fairness.evaluator"], [23, "module-cyclops.evaluate.metrics.accuracy"], [28, "module-cyclops.evaluate.metrics.auroc"], [33, "module-cyclops.evaluate.metrics.f_beta"], [42, "module-cyclops.evaluate.metrics.factory"], [44, "module-cyclops.evaluate.metrics.functional.accuracy"], [45, "module-cyclops.evaluate.metrics.functional.auroc"], [46, "module-cyclops.evaluate.metrics.functional.f_beta"], [55, "module-cyclops.evaluate.metrics.functional.precision_recall"], 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174, 192, 198], "befor": [1, 17, 21, 22, 143, 162, 182, 192, 198], "last": 1, "least": 1, "type": [1, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 127, 129, 131, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 178, 180, 182, 189, 194, 201], "hint": 1, "our": [1, 192, 198], "check": [1, 14, 89, 127, 170], "mypi": 1, "current": [1, 141, 178, 192, 195, 198], "ar": [1, 5, 7, 11, 12, 17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 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"cyclops.query.mimiciii.MIMICIIIQuerier": [[129, "cyclops-query-mimiciii-mimiciiiquerier"]], "cyclops.query.mimiciv": [[130, "module-cyclops.query.mimiciv"]], "cyclops.query.mimiciv.MIMICIVQuerier": [[131, "cyclops-query-mimiciv-mimicivquerier"]], "cyclops.query.omop": [[132, "module-cyclops.query.omop"]], "cyclops.query.omop.OMOPQuerier": [[133, "cyclops-query-omop-omopquerier"]], "cyclops.query.ops": [[134, "module-cyclops.query.ops"]], "cyclops.query.ops.AddColumn": [[135, "cyclops-query-ops-addcolumn"]], "cyclops.query.ops.AddDeltaColumn": [[136, "cyclops-query-ops-adddeltacolumn"]], "cyclops.query.ops.AddDeltaConstant": [[137, "cyclops-query-ops-adddeltaconstant"]], "cyclops.query.ops.AddNumeric": [[138, "cyclops-query-ops-addnumeric"]], "cyclops.query.ops.And": [[139, "cyclops-query-ops-and"]], "cyclops.query.ops.Apply": [[140, "cyclops-query-ops-apply"]], "cyclops.query.ops.Cast": [[141, "cyclops-query-ops-cast"]], "cyclops.query.ops.ConditionAfterDate": [[142, "cyclops-query-ops-conditionafterdate"]], "cyclops.query.ops.ConditionBeforeDate": [[143, "cyclops-query-ops-conditionbeforedate"]], "cyclops.query.ops.ConditionEndsWith": [[144, "cyclops-query-ops-conditionendswith"]], "cyclops.query.ops.ConditionEquals": [[145, "cyclops-query-ops-conditionequals"]], "cyclops.query.ops.ConditionGreaterThan": [[146, "cyclops-query-ops-conditiongreaterthan"]], "cyclops.query.ops.ConditionIn": [[147, "cyclops-query-ops-conditionin"]], "cyclops.query.ops.ConditionInMonths": [[148, "cyclops-query-ops-conditioninmonths"]], "cyclops.query.ops.ConditionInYears": [[149, "cyclops-query-ops-conditioninyears"]], "cyclops.query.ops.ConditionLessThan": [[150, "cyclops-query-ops-conditionlessthan"]], "cyclops.query.ops.ConditionLike": [[151, "cyclops-query-ops-conditionlike"]], "cyclops.query.ops.ConditionRegexMatch": [[152, "cyclops-query-ops-conditionregexmatch"]], "cyclops.query.ops.ConditionStartsWith": [[153, "cyclops-query-ops-conditionstartswith"]], "cyclops.query.ops.ConditionSubstring": [[154, "cyclops-query-ops-conditionsubstring"]], "cyclops.query.ops.Distinct": [[155, "cyclops-query-ops-distinct"]], "cyclops.query.ops.Drop": [[156, "cyclops-query-ops-drop"]], "cyclops.query.ops.DropEmpty": [[157, "cyclops-query-ops-dropempty"]], "cyclops.query.ops.DropNulls": [[158, "cyclops-query-ops-dropnulls"]], "cyclops.query.ops.ExtractTimestampComponent": [[159, "cyclops-query-ops-extracttimestampcomponent"]], "cyclops.query.ops.FillNull": [[160, "cyclops-query-ops-fillnull"]], "cyclops.query.ops.GroupByAggregate": [[161, "cyclops-query-ops-groupbyaggregate"]], "cyclops.query.ops.Join": [[162, "cyclops-query-ops-join"]], "cyclops.query.ops.Keep": [[163, "cyclops-query-ops-keep"]], "cyclops.query.ops.Limit": [[164, "cyclops-query-ops-limit"]], "cyclops.query.ops.Literal": [[165, "cyclops-query-ops-literal"]], "cyclops.query.ops.Or": [[166, "cyclops-query-ops-or"]], "cyclops.query.ops.OrderBy": [[167, "cyclops-query-ops-orderby"]], "cyclops.query.ops.QueryOp": [[168, "cyclops-query-ops-queryop"]], "cyclops.query.ops.RandomizeOrder": [[169, "cyclops-query-ops-randomizeorder"]], "cyclops.query.ops.Rename": [[170, "cyclops-query-ops-rename"]], "cyclops.query.ops.Reorder": [[171, "cyclops-query-ops-reorder"]], "cyclops.query.ops.ReorderAfter": [[172, "cyclops-query-ops-reorderafter"]], "cyclops.query.ops.Sequential": [[173, "cyclops-query-ops-sequential"]], "cyclops.query.ops.Substring": [[174, "cyclops-query-ops-substring"]], "cyclops.query.ops.Trim": [[175, "cyclops-query-ops-trim"]], "cyclops.query.ops.Union": [[176, "cyclops-query-ops-union"]], "cyclops.report.report": [[177, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[178, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[179, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[180, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[181, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[182, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[183, "module-cyclops.data"]], "cyclops.data.features": [[183, "module-cyclops.data.features"]], "cyclops.evaluate": [[184, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[184, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[184, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[184, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[185, "module-cyclops.monitor"]], "cyclops.query": [[186, "module-cyclops.query"]], "dataset APIs": [[186, "dataset-apis"]], "cyclops.report": [[187, "module-cyclops.report"]], "cyclops.tasks": [[188, "module-cyclops.tasks"]], "Tutorials": [[189, "tutorials"]], "eICU-CRD query API tutorial": [[190, "eICU-CRD-query-API-tutorial"]], "Imports and instantiate EICUQuerier": [[190, "Imports-and-instantiate-EICUQuerier"]], "Example 1. Get all female patients discharged in 2014 (limit to 100 rows).": [[190, "Example-1.-Get-all-female-patients-discharged-in-2014-(limit-to-100-rows)."]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in diagnosisstring), discharged in the year 2015.": [[190, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-diagnosisstring),-discharged-in-the-year-2015."]], "Example 3. Get potassium lab tests for patients discharged in the year 2014, for all teaching hospitals.": [[190, "Example-3.-Get-potassium-lab-tests-for-patients-discharged-in-the-year-2014,-for-all-teaching-hospitals."]], "Example 4. Get glucose medications (substring search) for female patients discharged in 2014.": [[190, "Example-4.-Get-glucose-medications-(substring-search)-for-female-patients-discharged-in-2014."]], "GEMINI query API tutorial": [[191, "GEMINI-query-API-tutorial"]], "Imports and instantiate GEMINIQuerier.": [[191, "Imports-and-instantiate-GEMINIQuerier."]], "Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by patient_id_hashed and discharge_date_time, and then keep the recent record.": [[191, "Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record."]], "Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.": [[191, "Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016."]], "Example 1c. From the above set of encounters, get the total number of admissions for each hospital.": [[191, "Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital."]], "Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?": [[191, "Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?"]], "Heart Failure Prediction": [[192, "Heart-Failure-Prediction"]], "Import Libraries": [[192, "Import-Libraries"], [195, "Import-Libraries"], [198, "Import-Libraries"]], "Constants": [[192, "Constants"], [198, "Constants"]], "Data Loading": [[192, "Data-Loading"]], "Sex values": [[192, "Sex-values"]], "Age distribution": [[192, "Age-distribution"], [198, "Age-distribution"]], "Outcome distribution": [[192, "Outcome-distribution"], [198, "Outcome-distribution"]], "Identifying feature types": [[192, "Identifying-feature-types"], [198, "Identifying-feature-types"]], "Creating data preprocessors": [[192, "Creating-data-preprocessors"], [198, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[192, "Creating-Hugging-Face-Dataset"], [198, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[192, "Model-Creation"], [198, "Model-Creation"]], "Task Creation": [[192, "Task-Creation"], [198, "Task-Creation"]], "Training": [[192, "Training"], [198, "Training"]], "Prediction": [[192, "Prediction"], [198, "Prediction"]], "Evaluation": [[192, "Evaluation"], [198, "Evaluation"]], "Performance over time": [[192, "Performance-over-time"], [195, "Performance-over-time"], [198, "Performance-over-time"]], "Report Generation": [[192, "Report-Generation"], [198, "Report-Generation"]], "MIMIC-III query API tutorial": [[193, "MIMIC-III-query-API-tutorial"]], "Imports and instantiate MIMICIIIQuerier": [[193, "Imports-and-instantiate-MIMICIIIQuerier"]], "Example 1. Get all male patients with a mortality outcome.": [[193, "Example-1.-Get-all-male-patients-with-a-mortality-outcome."]], "Example 2. Get all female patient encounters with diagnoses (gastroenteritis in ICD-9 long title).": [[193, "Example-2.-Get-all-female-patient-encounters-with-diagnoses-(gastroenteritis-in-ICD-9-long-title)."]], "Example 3. Get potassium lab tests for female patients.": [[193, "Example-3.-Get-potassium-lab-tests-for-female-patients."]], "Example 4. Get AaDO2 carevue chart events for male patients that have a valuenum of less than 20.": [[193, "Example-4.-Get-AaDO2-carevue-chart-events-for-male-patients-that-have-a-valuenum-of-less-than-20."]], "MIMIC-IV query API tutorial": [[194, "MIMIC-IV-query-API-tutorial"]], "Imports and instantiate MIMICIVQuerier": [[194, "Imports-and-instantiate-MIMICIVQuerier"]], "Example 1. Get all patient admissions from 2021 or later (approx year of admission)": [[194, "Example-1.-Get-all-patient-admissions-from-2021-or-later-(approx-year-of-admission)"]], "Example 2. Get all patient encounters with diagnoses (schizophrenia in ICD-10 long title), in the year 2015.": [[194, "Example-2.-Get-all-patient-encounters-with-diagnoses-(schizophrenia-in-ICD-10-long-title),-in-the-year-2015."]], "Example 3. Advanced - uses ConditionRegexMatch from cyclops.query.ops. Get all patient encounters with diagnoses (ICD-9 long title contains schizophrenia and chronic ), in the year 2015.": [[194, "Example-3.-Advanced---uses-ConditionRegexMatch-from-cyclops.query.ops.-Get-all-patient-encounters-with-diagnoses-(ICD-9-long-title-contains-schizophrenia-and-chronic-),-in-the-year-2015."]], "Example 4. Get routine vital signs for patients from year 2015.": [[194, "Example-4.-Get-routine-vital-signs-for-patients-from-year-2015."]], "Example 5. Get hemoglobin lab tests for patients from year 2009.": [[194, "Example-5.-Get-hemoglobin-lab-tests-for-patients-from-year-2009."]], "Example 6. Get radiology reports and filter on keywords lymphadenopathy and infectious occurring together from year 2009.": [[194, "Example-6.-Get-radiology-reports-and-filter-on-keywords-lymphadenopathy-and-infectious-occurring-together-from-year-2009."]], "Example 7. Get all female patient encounters from year 2015, and return as dask dataframe (lazy evaluation) with 4 partitions (batches) aggregated based on subject_id.": [[194, "Example-7.-Get-all-female-patient-encounters-from-year-2015,-and-return-as-dask-dataframe-(lazy-evaluation)-with-4-partitions-(batches)-aggregated-based-on-subject_id."]], "Example 8. Running a raw SQL string.": [[194, "Example-8.-Running-a-raw-SQL-string."]], "Chest X-Ray Disease Classification": [[195, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[195, "Load-Dataset"]], "Load Model and get Predictions": [[195, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[195, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[195, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[195, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[195, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[195, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[195, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[196, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[196, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[196, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[196, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[196, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[196, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[196, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "OMOP query API tutorial": [[197, "OMOP-query-API-tutorial"]], "Imports and instantiate OMOPQuerier.": [[197, "Imports-and-instantiate-OMOPQuerier."], [197, "id1"]], "Example 1. Get all patient visits in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-in-or-after-2010."]], "Example 2. Get measurements for all visits in or after 2020.": [[197, "Example-2.-Get-measurements-for-all-visits-in-or-after-2020."]], "Example 1. Get all patient visits that ended in a mortality outcome in or after 2010.": [[197, "Example-1.-Get-all-patient-visits-that-ended-in-a-mortality-outcome-in-or-after-2010."]], "Example 2. Get all measurements for female patient visits with sepsis diagnoses, that ended in a mortality outcome.": [[197, "Example-2.-Get-all-measurements-for-female-patient-visits-with-sepsis-diagnoses,-that-ended-in-a-mortality-outcome."]], "Prolonged Length of Stay Prediction": [[198, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[198, "Data-Querying"]], "Compute length of stay (labels)": [[198, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[198, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[198, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[198, "Length-of-stay-distribution"]], "Gender distribution": [[198, "Gender-distribution"]], "monitor API": [[199, "monitor-api"]], "query API": [[200, "query-api"]], "Example use cases": [[201, "example-use-cases"]], "Tabular data": [[201, "tabular-data"]], "Kaggle Heart Failure Prediction": [[201, "kaggle-heart-failure-prediction"]], "Synthea 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"module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[184, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[185, "module-cyclops.monitor"]], "cyclops.query": [[186, "module-cyclops.query"]], "cyclops.report": [[187, "module-cyclops.report"]], "cyclops.tasks": [[188, "module-cyclops.tasks"]]}})
\ No newline at end of file
diff --git a/api/tutorials.html b/api/tutorials.html
index 06de3ff77..95c94f453 100644
--- a/api/tutorials.html
+++ b/api/tutorials.html
@@ -215,7 +215,7 @@ <h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading
 <li class="toctree-l3"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record.">Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by <code class="docutils literal notranslate"><span class="pre">patient_id_hashed</span></code> and <code class="docutils literal notranslate"><span class="pre">discharge_date_time</span></code>, and then keep the recent record.</a></li>
 <li class="toctree-l3"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016.">Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.</a></li>
 <li class="toctree-l3"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital.">Example 1c. From the above set of encounters, get the total number of admissions for each hospital.</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?</a></li>
 </ul>
 </li>
 </ul>
diff --git a/api/tutorials/eicu/query_api.html b/api/tutorials/eicu/query_api.html
index a029add91..4cfc92667 100644
--- a/api/tutorials/eicu/query_api.html
+++ b/api/tutorials/eicu/query_api.html
@@ -483,9 +483,9 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-19 11:57:25,627 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:13:32,085 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -549,8 +549,8 @@ <h2>Example 1. Get all female patients discharged in 2014 (limit to 100 rows).<a
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:26,109 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:26,111 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.030195 s
+2023-09-21 11:13:32,731 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:13:32,732 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.042269 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -585,8 +585,8 @@ <h2>Example 2. Get all patient encounters with diagnoses (<code class="docutils
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:26,236 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:26,238 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.107824 s
+2023-09-21 11:13:32,811 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:13:32,812 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.068682 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -627,8 +627,8 @@ <h2>Example 3. Get potassium lab tests for patients discharged in the year 2014,
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:26,299 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:26,300 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.031113 s
+2023-09-21 11:13:32,883 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:13:32,884 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.036568 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -667,8 +667,8 @@ <h2>Example 4. Get glucose medications (substring search) for female patients di
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:26,463 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:26,464 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.146213 s
+2023-09-21 11:13:33,061 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:13:33,062 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.158932 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/api/tutorials/eicu/query_api.ipynb b/api/tutorials/eicu/query_api.ipynb
index 37ad1ffe9..10855de68 100644
--- a/api/tutorials/eicu/query_api.ipynb
+++ b/api/tutorials/eicu/query_api.ipynb
@@ -35,10 +35,10 @@
    "id": "75a140e0-fb27-4319-862f-be54397abe5c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:23.278323Z",
-     "iopub.status.busy": "2023-09-19T15:57:23.277495Z",
-     "iopub.status.idle": "2023-09-19T15:57:26.071143Z",
-     "shell.execute_reply": "2023-09-19T15:57:26.069546Z"
+     "iopub.execute_input": "2023-09-21T15:13:29.994261Z",
+     "iopub.status.busy": "2023-09-21T15:13:29.993598Z",
+     "iopub.status.idle": "2023-09-21T15:13:32.675511Z",
+     "shell.execute_reply": "2023-09-21T15:13:32.674003Z"
     },
     "tags": []
    },
@@ -47,7 +47,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -55,7 +55,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:25,627 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:13:32,085 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
@@ -132,10 +132,10 @@
    "id": "c1efa964-8978-4a0e-9892-5ea4ce9953a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:26.075298Z",
-     "iopub.status.busy": "2023-09-19T15:57:26.074723Z",
-     "iopub.status.idle": "2023-09-19T15:57:26.115883Z",
-     "shell.execute_reply": "2023-09-19T15:57:26.114669Z"
+     "iopub.execute_input": "2023-09-21T15:13:32.680552Z",
+     "iopub.status.busy": "2023-09-21T15:13:32.679674Z",
+     "iopub.status.idle": "2023-09-21T15:13:32.735778Z",
+     "shell.execute_reply": "2023-09-21T15:13:32.735178Z"
     },
     "tags": []
    },
@@ -144,14 +144,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,109 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:13:32,731 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,111 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.030195 s\n"
+      "2023-09-21 11:13:32,732 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.042269 s\n"
      ]
     },
     {
@@ -188,10 +188,10 @@
    "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:26.123572Z",
-     "iopub.status.busy": "2023-09-19T15:57:26.123270Z",
-     "iopub.status.idle": "2023-09-19T15:57:26.242540Z",
-     "shell.execute_reply": "2023-09-19T15:57:26.241344Z"
+     "iopub.execute_input": "2023-09-21T15:13:32.739071Z",
+     "iopub.status.busy": "2023-09-21T15:13:32.738807Z",
+     "iopub.status.idle": "2023-09-21T15:13:32.815828Z",
+     "shell.execute_reply": "2023-09-21T15:13:32.814793Z"
     },
     "tags": []
    },
@@ -200,14 +200,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,236 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:13:32,811 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,238 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.107824 s\n"
+      "2023-09-21 11:13:32,812 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.068682 s\n"
      ]
     },
     {
@@ -244,10 +244,10 @@
    "id": "24043abc-1878-4e00-8229-36d4a0368b98",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:26.253461Z",
-     "iopub.status.busy": "2023-09-19T15:57:26.253121Z",
-     "iopub.status.idle": "2023-09-19T15:57:26.303996Z",
-     "shell.execute_reply": "2023-09-19T15:57:26.303249Z"
+     "iopub.execute_input": "2023-09-21T15:13:32.820582Z",
+     "iopub.status.busy": "2023-09-21T15:13:32.819523Z",
+     "iopub.status.idle": "2023-09-21T15:13:32.888583Z",
+     "shell.execute_reply": "2023-09-21T15:13:32.887723Z"
     },
     "tags": []
    },
@@ -256,14 +256,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,299 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:13:32,883 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,300 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.031113 s\n"
+      "2023-09-21 11:13:32,884 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.036568 s\n"
      ]
     },
     {
@@ -306,10 +306,10 @@
    "id": "f6142f27-e8d1-453c-bfe2-2265d9ff1914",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:26.308569Z",
-     "iopub.status.busy": "2023-09-19T15:57:26.308289Z",
-     "iopub.status.idle": "2023-09-19T15:57:26.468529Z",
-     "shell.execute_reply": "2023-09-19T15:57:26.467651Z"
+     "iopub.execute_input": "2023-09-21T15:13:32.892256Z",
+     "iopub.status.busy": "2023-09-21T15:13:32.891751Z",
+     "iopub.status.idle": "2023-09-21T15:13:33.070327Z",
+     "shell.execute_reply": "2023-09-21T15:13:33.068395Z"
     },
     "tags": []
    },
@@ -318,14 +318,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,463 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:13:33,061 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:26,464 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.146213 s\n"
+      "2023-09-21 11:13:33,062 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.158932 s\n"
      ]
     },
     {
diff --git a/api/tutorials/gemini/query_api.html b/api/tutorials/gemini/query_api.html
index 803321697..e477891ae 100644
--- a/api/tutorials/gemini/query_api.html
+++ b/api/tutorials/gemini/query_api.html
@@ -137,7 +137,7 @@
 <li class="toctree-l4"><a class="reference internal" href="#Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record.">Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by <code class="docutils literal notranslate"><span class="pre">patient_id_hashed</span></code> and <code class="docutils literal notranslate"><span class="pre">discharge_date_time</span></code>, and then keep the recent record.</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016.">Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital.">Example 1c. From the above set of encounters, get the total number of admissions for each hospital.</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?</a></li>
 </ul>
 </li>
 </ul>
@@ -461,12 +461,12 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 
 <span class="n">querier</span> <span class="o">=</span> <span class="n">GEMINIQuerier</span><span class="p">(</span>
     <span class="n">host</span><span class="o">=</span><span class="s2">&quot;db.gemini-hpc.ca&quot;</span><span class="p">,</span>
-    <span class="n">database</span><span class="o">=</span><span class="s2">&quot;delirium_v3_0_0&quot;</span><span class="p">,</span>
+    <span class="n">database</span><span class="o">=</span><span class="s2">&quot;delirium_v4_0_1&quot;</span><span class="p">,</span>
     <span class="n">user</span><span class="o">=</span><span class="s2">&quot;username&quot;</span><span class="p">,</span>
     <span class="n">password</span><span class="o">=</span><span class="s2">&quot;password&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="c1"># List all tables.</span>
-<span class="n">querier</span><span class="o">.</span><span class="n">list_tables</span><span class="p">()</span>
+<span class="n">querier</span><span class="o">.</span><span class="n">list_tables</span><span class="p">(</span><span class="s2">&quot;public&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -475,7 +475,7 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-03-21 17:09:15,267 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 09:17:45,449 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -484,30 +484,40 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-[&#39;information_schema.sql_implementation_info&#39;,
- &#39;information_schema.sql_parts&#39;,
- &#39;information_schema.sql_sizing&#39;,
- &#39;information_schema.sql_features&#39;,
- &#39;public.ip_administrative&#39;,
+[&#39;public.lookup_icd10_ca_description&#39;,
+ &#39;public.lookup_statcan&#39;,
+ &#39;public.lookup_cci&#39;,
+ &#39;public.lookup_icd10_ca_to_ccsr&#39;,
+ &#39;public.lookup_ip_administrative&#39;,
+ &#39;public.lookup_lab_concept&#39;,
+ &#39;public.lookup_vitals_concept&#39;,
+ &#39;public.lookup_pharmacy_concept&#39;,
+ &#39;public.lookup_diagnosis&#39;,
+ &#39;public.locality_variables&#39;,
+ &#39;public.admdad&#39;,
  &#39;public.derived_variables&#39;,
- &#39;public.diagnosis&#39;,
- &#39;public.vitals&#39;,
- &#39;public.intervention&#39;,
- &#39;public.er_administrative&#39;,
- &#39;public.imaging&#39;,
- &#39;public.pharmacy&#39;,
+ &#39;public.ipscu&#39;,
+ &#39;public.lookup_phy_characteristics&#39;,
  &#39;public.lab&#39;,
- &#39;public.room_transfer&#39;,
- &#39;public.ip_scu&#39;,
- &#39;public.lookup_ip_administrative&#39;,
+ &#39;public.ipintervention&#39;,
  &#39;public.lookup_ccsr&#39;,
- &#39;public.hospital&#39;,
- &#39;public.lookup_er_administrative&#39;,
- &#39;public.blood_transfusion&#39;,
+ &#39;public.lookup_pharmacy_route&#39;,
+ &#39;public.lookup_transfusion_concept&#39;,
  &#39;public.lookup_ip_scu&#39;,
- &#39;public.lookup_diagnosis&#39;,
+ &#39;public.lookup_er_administrative&#39;,
+ &#39;public.lookup_imaging&#39;,
+ &#39;public.pharmacy&#39;,
+ &#39;public.radiology&#39;,
+ &#39;public.lookup_transfer&#39;,
+ &#39;public.ipdiagnosis&#39;,
  &#39;public.lookup_room_transfer&#39;,
- &#39;public.lookup_imaging&#39;]
+ &#39;public.er&#39;,
+ &#39;public.erdiagnosis&#39;,
+ &#39;public.erintervention&#39;,
+ &#39;public.roomtransfer&#39;,
+ &#39;public.transfusion&#39;,
+ &#39;public.vitals&#39;,
+ &#39;public.lookup_hospital_num&#39;]
 </pre></div></div>
 </div>
 </section>
@@ -524,8 +534,8 @@ <h2>Example 1a. Create a table with only one hospitalization per patient, keepin
     <span class="p">),</span>
     <span class="n">qo</span><span class="o">.</span><span class="n">Distinct</span><span class="p">(</span><span class="s2">&quot;patient_id_hashed&quot;</span><span class="p">),</span>
 <span class="p">)</span>
-<span class="n">encounters_qi</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">ip_administrative</span><span class="p">(</span><span class="n">ops</span><span class="o">=</span><span class="n">ops</span><span class="p">)</span>
-<span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters_qi</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="n">encounters</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">admdad</span><span class="p">()</span>
+<span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">encounters</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows extracted!&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -535,8 +545,8 @@ <h2>Example 1a. Create a table with only one hospitalization per patient, keepin
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-03-21 17:09:20,365 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-03-21 17:09:20,366 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 4.825447 s
+2023-09-21 09:17:51,902 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 09:17:51,903 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 6.093352 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -544,7 +554,7 @@ <h2>Example 1a. Create a table with only one hospitalization per patient, keepin
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-189606 rows extracted!
+189734 rows extracted!
 </pre></div></div>
 </div>
 </section>
@@ -555,11 +565,13 @@ <h2>Example 1b. From the above set of encounters, take a subset of patients who
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">Cast</span><span class="p">(</span><span class="s2">&quot;discharge_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">),</span>
     <span class="n">qo</span><span class="o">.</span><span class="n">ConditionAfterDate</span><span class="p">(</span><span class="s2">&quot;discharge_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2015-04-01&quot;</span><span class="p">),</span>
     <span class="n">qo</span><span class="o">.</span><span class="n">ConditionBeforeDate</span><span class="p">(</span><span class="s2">&quot;discharge_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2016-03-31&quot;</span><span class="p">),</span>
 <span class="p">)</span>
-<span class="n">encounters_qi</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">get_interface</span><span class="p">(</span><span class="n">encounters_qi</span><span class="o">.</span><span class="n">query</span><span class="p">,</span> <span class="n">ops</span><span class="o">=</span><span class="n">ops</span><span class="p">)</span>
-<span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters_qi</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="n">encounters_query</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">admdad</span><span class="p">()</span>
+<span class="n">encounters_query</span> <span class="o">=</span> <span class="n">encounters_query</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span>
+<span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters_query</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">encounters</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows extracted!&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -569,8 +581,8 @@ <h2>Example 1b. From the above set of encounters, take a subset of patients who
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-03-21 17:09:21,372 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-03-21 17:09:21,373 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.992901 s
+2023-09-21 09:17:52,591 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 09:17:52,592 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.675141 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -578,7 +590,7 @@ <h2>Example 1b. From the above set of encounters, take a subset of patients who
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-17071 rows extracted!
+32567 rows extracted!
 </pre></div></div>
 </div>
 </section>
@@ -588,9 +600,8 @@ <h2>Example 1c. From the above set of encounters, get the total number of admiss
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span><span class="s2">&quot;hospital_id&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;hospital_id&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;count&quot;</span><span class="p">)})</span>
-<span class="n">encounters_per_site_qi</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">get_interface</span><span class="p">(</span><span class="n">encounters_qi</span><span class="o">.</span><span class="n">query</span><span class="p">,</span> <span class="n">ops</span><span class="o">=</span><span class="n">ops</span><span class="p">)</span>
-<span class="n">encounters_per_site</span> <span class="o">=</span> <span class="n">encounters_per_site_qi</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span><span class="s2">&quot;hospital_num&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;hospital_num&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;count&quot;</span><span class="p">)})</span>
+<span class="n">encounters_per_site</span> <span class="o">=</span> <span class="n">encounters_query</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">encounters_per_site</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows extracted!&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -600,8 +611,8 @@ <h2>Example 1c. From the above set of encounters, get the total number of admiss
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-03-21 17:09:22,181 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-03-21 17:09:22,182 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.800708 s
+2023-09-21 09:17:52,856 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 09:17:52,857 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.145693 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -613,25 +624,28 @@ <h2>Example 1c. From the above set of encounters, get the total number of admiss
 </pre></div></div>
 </div>
 </section>
-<section id="Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?">
-<h2>Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?<a class="headerlink" href="#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?" title="Link to this heading"></a></h2>
+<section id="Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?">
+<h2>Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?<a class="headerlink" href="#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?" title="Link to this heading"></a></h2>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">encounter_ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">ConditionEquals</span><span class="p">(</span><span class="s2">&quot;hospital_id&quot;</span><span class="p">,</span> <span class="s2">&quot;SMH&quot;</span><span class="p">)</span>
-<span class="n">encounters_qi</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">ip_administrative</span><span class="p">(</span><span class="n">ops</span><span class="o">=</span><span class="n">encounter_ops</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">encounters</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">admdad</span><span class="p">()</span>
+<span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">qo</span><span class="o">.</span><span class="n">ConditionEquals</span><span class="p">(</span><span class="s2">&quot;hospital_num&quot;</span><span class="p">,</span> <span class="mi">101</span><span class="p">))</span>
 <span class="n">lab_ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionAfterDate</span><span class="p">(</span><span class="s2">&quot;sample_collection_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2015-04-01&quot;</span><span class="p">),</span>
-    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionBeforeDate</span><span class="p">(</span><span class="s2">&quot;sample_collection_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2015-05-31&quot;</span><span class="p">),</span>
-    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionSubstring</span><span class="p">(</span><span class="s2">&quot;lab_test_name_mapped&quot;</span><span class="p">,</span> <span class="s2">&quot;sodium&quot;</span><span class="p">),</span>
-    <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span><span class="s2">&quot;hospital_id&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;hospital_id&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;count&quot;</span><span class="p">)}),</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">DropEmpty</span><span class="p">(</span><span class="s2">&quot;collection_date_time&quot;</span><span class="p">),</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">Cast</span><span class="p">(</span><span class="s2">&quot;collection_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">),</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionAfterDate</span><span class="p">(</span><span class="s2">&quot;collection_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2015-04-01&quot;</span><span class="p">),</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionBeforeDate</span><span class="p">(</span><span class="s2">&quot;collection_date_time&quot;</span><span class="p">,</span> <span class="s2">&quot;2015-05-31&quot;</span><span class="p">),</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">ConditionSubstring</span><span class="p">(</span><span class="s2">&quot;test_type_mapped&quot;</span><span class="p">,</span> <span class="s2">&quot;sodium&quot;</span><span class="p">),</span>
 <span class="p">)</span>
-<span class="n">lab_qi</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">lab</span><span class="p">(</span>
-    <span class="n">ops</span><span class="o">=</span><span class="n">lab_ops</span><span class="p">,</span>
-    <span class="n">join</span><span class="o">=</span><span class="n">qo</span><span class="o">.</span><span class="n">JoinArgs</span><span class="p">(</span><span class="n">join_table</span><span class="o">=</span><span class="n">encounters_qi</span><span class="o">.</span><span class="n">query</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;genc_id&quot;</span><span class="p">),</span>
+<span class="n">labs</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">public</span><span class="o">.</span><span class="n">lab</span><span class="p">()</span>
+<span class="n">labs</span> <span class="o">=</span> <span class="n">labs</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">lab_ops</span><span class="p">)</span>
+<span class="n">encounters_labs</span> <span class="o">=</span> <span class="n">encounters</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">labs</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;genc_id&quot;</span><span class="p">)</span>
+<span class="n">encounters_labs</span> <span class="o">=</span> <span class="n">encounters_labs</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span>
+    <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span><span class="s2">&quot;hospital_num&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;hospital_num&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;count&quot;</span><span class="p">)}),</span>
 <span class="p">)</span>
-<span class="n">sodium_tests</span> <span class="o">=</span> <span class="n">lab_qi</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="n">sodium_tests</span> <span class="o">=</span> <span class="n">encounters_labs</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">sodium_tests</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows extracted!&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">sodium_tests</span><span class="p">)</span>
 </pre></div>
@@ -642,55 +656,20 @@ <h2>Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-03-21 17:09:59,972 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-03-21 17:09:59,973 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 37.740935 s
+2023-09-21 09:26:19,814 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 09:26:19,815 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 506.939296 s
 </pre></div></div>
 </div>
-<div class="nboutput docutils container">
+<div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 1 rows extracted!
+   hospital_num  count
+0           101   9305
 </pre></div></div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>hospital_id</th>
-      <th>count</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>SMH</td>
-      <td>6053</td>
-    </tr>
-  </tbody>
-</table>
-</div></div>
-</div>
 </section>
 </section>
 
diff --git a/api/tutorials/gemini/query_api.ipynb b/api/tutorials/gemini/query_api.ipynb
index 1a3f4c8b9..ce7c159aa 100644
--- a/api/tutorials/gemini/query_api.ipynb
+++ b/api/tutorials/gemini/query_api.ipynb
@@ -26,36 +26,46 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:15,267 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 09:17:45,449 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['information_schema.sql_implementation_info',\n",
-       " 'information_schema.sql_parts',\n",
-       " 'information_schema.sql_sizing',\n",
-       " 'information_schema.sql_features',\n",
-       " 'public.ip_administrative',\n",
+       "['public.lookup_icd10_ca_description',\n",
+       " 'public.lookup_statcan',\n",
+       " 'public.lookup_cci',\n",
+       " 'public.lookup_icd10_ca_to_ccsr',\n",
+       " 'public.lookup_ip_administrative',\n",
+       " 'public.lookup_lab_concept',\n",
+       " 'public.lookup_vitals_concept',\n",
+       " 'public.lookup_pharmacy_concept',\n",
+       " 'public.lookup_diagnosis',\n",
+       " 'public.locality_variables',\n",
+       " 'public.admdad',\n",
        " 'public.derived_variables',\n",
-       " 'public.diagnosis',\n",
-       " 'public.vitals',\n",
-       " 'public.intervention',\n",
-       " 'public.er_administrative',\n",
-       " 'public.imaging',\n",
-       " 'public.pharmacy',\n",
+       " 'public.ipscu',\n",
+       " 'public.lookup_phy_characteristics',\n",
        " 'public.lab',\n",
-       " 'public.room_transfer',\n",
-       " 'public.ip_scu',\n",
-       " 'public.lookup_ip_administrative',\n",
+       " 'public.ipintervention',\n",
        " 'public.lookup_ccsr',\n",
-       " 'public.hospital',\n",
-       " 'public.lookup_er_administrative',\n",
-       " 'public.blood_transfusion',\n",
+       " 'public.lookup_pharmacy_route',\n",
+       " 'public.lookup_transfusion_concept',\n",
        " 'public.lookup_ip_scu',\n",
-       " 'public.lookup_diagnosis',\n",
+       " 'public.lookup_er_administrative',\n",
+       " 'public.lookup_imaging',\n",
+       " 'public.pharmacy',\n",
+       " 'public.radiology',\n",
+       " 'public.lookup_transfer',\n",
+       " 'public.ipdiagnosis',\n",
        " 'public.lookup_room_transfer',\n",
-       " 'public.lookup_imaging']"
+       " 'public.er',\n",
+       " 'public.erdiagnosis',\n",
+       " 'public.erintervention',\n",
+       " 'public.roomtransfer',\n",
+       " 'public.transfusion',\n",
+       " 'public.vitals',\n",
+       " 'public.lookup_hospital_num']"
       ]
      },
      "execution_count": 1,
@@ -72,12 +82,12 @@
     "\n",
     "querier = GEMINIQuerier(\n",
     "    host=\"db.gemini-hpc.ca\",\n",
-    "    database=\"delirium_v3_0_0\",\n",
+    "    database=\"delirium_v4_0_1\",\n",
     "    user=\"username\",\n",
     "    password=\"password\",\n",
     ")\n",
     "# List all tables.\n",
-    "querier.list_tables()"
+    "querier.list_tables(\"public\")"
    ]
   },
   {
@@ -98,15 +108,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:20,365 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:20,366 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 4.825447 s\n"
+      "2023-09-21 09:17:51,902 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:51,903 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 6.093352 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "189606 rows extracted!\n"
+      "189734 rows extracted!\n"
      ]
     }
    ],
@@ -118,8 +128,8 @@
     "    ),\n",
     "    qo.Distinct(\"patient_id_hashed\"),\n",
     ")\n",
-    "encounters_qi = querier.public.ip_administrative(ops=ops)\n",
-    "encounters = encounters_qi.run()\n",
+    "encounters = querier.public.admdad()\n",
+    "encounters = encounters.ops(ops).run()\n",
     "print(f\"{len(encounters)} rows extracted!\")"
    ]
   },
@@ -141,25 +151,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:21,372 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:21,373 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.992901 s\n"
+      "2023-09-21 09:17:52,591 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:52,592 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.675141 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17071 rows extracted!\n"
+      "32567 rows extracted!\n"
      ]
     }
    ],
    "source": [
     "ops = qo.Sequential(\n",
+    "    qo.Cast(\"discharge_date_time\", \"timestamp\"),\n",
     "    qo.ConditionAfterDate(\"discharge_date_time\", \"2015-04-01\"),\n",
     "    qo.ConditionBeforeDate(\"discharge_date_time\", \"2016-03-31\"),\n",
     ")\n",
-    "encounters_qi = querier.get_interface(encounters_qi.query, ops=ops)\n",
-    "encounters = encounters_qi.run()\n",
+    "encounters_query = querier.public.admdad()\n",
+    "encounters_query = encounters_query.ops(ops)\n",
+    "encounters = encounters_query.run()\n",
     "print(f\"{len(encounters)} rows extracted!\")"
    ]
   },
@@ -181,8 +193,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:22,181 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:22,182 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.800708 s\n"
+      "2023-09-21 09:17:52,856 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:17:52,857 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.145693 s\n"
      ]
     },
     {
@@ -194,9 +206,8 @@
     }
    ],
    "source": [
-    "ops = qo.GroupByAggregate(\"hospital_id\", {\"hospital_id\": (\"count\", \"count\")})\n",
-    "encounters_per_site_qi = querier.get_interface(encounters_qi.query, ops=ops)\n",
-    "encounters_per_site = encounters_per_site_qi.run()\n",
+    "ops = qo.GroupByAggregate(\"hospital_num\", {\"hospital_num\": (\"count\", \"count\")})\n",
+    "encounters_per_site = encounters_query.ops(ops).run()\n",
     "print(f\"{len(encounters_per_site)} rows extracted!\")"
    ]
   },
@@ -205,7 +216,7 @@
    "id": "848c5b90-1e66-4a5a-9ebb-9c0caa105c3f",
    "metadata": {},
    "source": [
-    "## Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?"
+    "## Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?"
    ]
   },
   {
@@ -218,76 +229,37 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-03-21 17:09:59,972 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
-      "2023-03-21 17:09:59,973 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 37.740935 s\n"
+      "2023-09-21 09:26:19,814 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n",
+      "2023-09-21 09:26:19,815 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 506.939296 s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 rows extracted!\n"
+      "1 rows extracted!\n",
+      "   hospital_num  count\n",
+      "0           101   9305\n"
      ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hospital_id</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>SMH</td>\n",
-       "      <td>6053</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  hospital_id  count\n",
-       "0         SMH   6053"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "encounter_ops = qo.ConditionEquals(\"hospital_id\", \"SMH\")\n",
-    "encounters_qi = querier.public.ip_administrative(ops=encounter_ops)\n",
+    "encounters = querier.public.admdad()\n",
+    "encounters = encounters.ops(qo.ConditionEquals(\"hospital_num\", 101))\n",
     "lab_ops = qo.Sequential(\n",
-    "    qo.ConditionAfterDate(\"sample_collection_date_time\", \"2015-04-01\"),\n",
-    "    qo.ConditionBeforeDate(\"sample_collection_date_time\", \"2015-05-31\"),\n",
-    "    qo.ConditionSubstring(\"lab_test_name_mapped\", \"sodium\"),\n",
-    "    qo.GroupByAggregate(\"hospital_id\", {\"hospital_id\": (\"count\", \"count\")}),\n",
+    "    qo.DropEmpty(\"collection_date_time\"),\n",
+    "    qo.Cast(\"collection_date_time\", \"timestamp\"),\n",
+    "    qo.ConditionAfterDate(\"collection_date_time\", \"2015-04-01\"),\n",
+    "    qo.ConditionBeforeDate(\"collection_date_time\", \"2015-05-31\"),\n",
+    "    qo.ConditionSubstring(\"test_type_mapped\", \"sodium\"),\n",
     ")\n",
-    "lab_qi = querier.public.lab(\n",
-    "    ops=lab_ops,\n",
-    "    join=qo.JoinArgs(join_table=encounters_qi.query, on=\"genc_id\"),\n",
+    "labs = querier.public.lab()\n",
+    "labs = labs.ops(lab_ops)\n",
+    "encounters_labs = encounters.join(labs, on=\"genc_id\")\n",
+    "encounters_labs = encounters_labs.ops(\n",
+    "    qo.GroupByAggregate(\"hospital_num\", {\"hospital_num\": (\"count\", \"count\")}),\n",
     ")\n",
-    "sodium_tests = lab_qi.run()\n",
+    "sodium_tests = encounters_labs.run()\n",
     "print(f\"{len(sodium_tests)} rows extracted!\")\n",
     "print(sodium_tests)"
    ]
diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html
index 0dd4443b8..14fe54e78 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ b/api/tutorials/kaggle/heart_failure_prediction.html
@@ -486,7 +486,7 @@ <h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link t
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 </pre></div></div>
 </div>
@@ -550,7 +550,7 @@ <h2>Data Loading<a class="headerlink" href="#Data-Loading" title="Link to this h
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:34,703 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-09-21 11:13:41,041 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.file - Loading DataFrame from ./data/heart.csv
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -632,9 +632,9 @@ <h3>Sex values<a class="headerlink" href="#Sex-values" title="Link to this headi
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
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+{&#39;RestingECG&#39;: &#39;ordinal&#39;, &#39;MaxHR&#39;: &#39;numeric&#39;, &#39;Sex&#39;: &#39;binary&#39;, &#39;FastingBS&#39;: &#39;binary&#39;, &#39;outcome&#39;: &#39;binary&#39;, &#39;ExerciseAngina&#39;: &#39;binary&#39;, &#39;ChestPainType&#39;: &#39;ordinal&#39;, &#39;RestingBP&#39;: &#39;numeric&#39;, &#39;Cholesterol&#39;: &#39;numeric&#39;, &#39;Oldpeak&#39;: &#39;numeric&#39;, &#39;Age&#39;: &#39;numeric&#39;, &#39;ST_Slope&#39;: &#39;ordinal&#39;}
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-Casting the dataset: 100%|██████████| 918/918 [00:00&lt;00:00, 172739.84 examples/s]
+Casting the dataset: 100%|██████████| 918/918 [00:00&lt;00:00, 187438.96 examples/s]
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@@ -1131,9 +1131,9 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2023-09-19 11:57:42,363 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best alpha: 0.001
-2023-09-19 11:57:42,364 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best eta0: 0.01
-2023-09-19 11:57:42,364 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
+2023-09-21 11:13:48,423 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best alpha: 0.001
+2023-09-21 11:13:48,424 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best eta0: 0.01
+2023-09-21 11:13:48,425 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
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-Map: 100%|██████████| 184/184 [00:00&lt;00:00, 8670.20 examples/s]
+Map: 100%|██████████| 184/184 [00:00&lt;00:00, 7778.74 examples/s]
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-Map: 100%|██████████| 184/184 [00:00&lt;00:00, 7813.47 examples/s]
-Flattening the indices: 100%|██████████| 184/184 [00:00&lt;00:00, 7201.19 examples/s]
-Flattening the indices: 100%|██████████| 184/184 [00:00&lt;00:00, 38651.37 examples/s]
-Filter -&gt; Age:[30 - 50): 100%|██████████| 184/184 [00:00&lt;00:00, 10658.09 examples/s]
-Filter -&gt; Age:[50 - 70): 100%|██████████| 184/184 [00:00&lt;00:00, 13689.86 examples/s]
-Filter -&gt; overall: 100%|██████████| 184/184 [00:00&lt;00:00, 23055.95 examples/s]
-Filter -&gt; Age:(-inf - 40.0]: 100%|██████████| 184/184 [00:00&lt;00:00, 20243.74 examples/s]
-Filter -&gt; Age:(40.0 - 50.0]: 100%|██████████| 184/184 [00:00&lt;00:00, 20923.76 examples/s]
-Filter -&gt; Age:(50.0 - inf]: 100%|██████████| 184/184 [00:00&lt;00:00, 20995.48 examples/s]
+Map: 100%|██████████| 184/184 [00:00&lt;00:00, 7153.93 examples/s]
+Flattening the indices: 100%|██████████| 184/184 [00:00&lt;00:00, 8233.78 examples/s]
+Flattening the indices: 100%|██████████| 184/184 [00:00&lt;00:00, 48981.46 examples/s]
+Filter -&gt; Age:[30 - 50): 100%|██████████| 184/184 [00:00&lt;00:00, 19480.81 examples/s]
+Filter -&gt; Age:[50 - 70): 100%|██████████| 184/184 [00:00&lt;00:00, 15568.62 examples/s]
+Filter -&gt; overall: 100%|██████████| 184/184 [00:00&lt;00:00, 21303.81 examples/s]
+Filter -&gt; Age:(-inf - 40.0]: 100%|██████████| 184/184 [00:00&lt;00:00, 20113.94 examples/s]
+Filter -&gt; Age:(40.0 - 50.0]: 100%|██████████| 184/184 [00:00&lt;00:00, 21511.65 examples/s]
+Filter -&gt; Age:(50.0 - inf]: 100%|██████████| 184/184 [00:00&lt;00:00, 20855.91 examples/s]
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diff --git a/api/tutorials/kaggle/heart_failure_prediction.ipynb b/api/tutorials/kaggle/heart_failure_prediction.ipynb
index 9c24d99ed..a7bb6d764 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.ipynb
+++ b/api/tutorials/kaggle/heart_failure_prediction.ipynb
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@@ -2100,10 +2100,10 @@
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-     "shell.execute_reply": "2023-09-19T15:57:35.354249Z"
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@@ -2127,10 +2127,10 @@
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-     "shell.execute_reply": "2023-09-19T15:57:35.412063Z"
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@@ -3933,9 +3933,9 @@
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@@ -3988,10 +3988,10 @@
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-     "shell.execute_reply": "2023-09-19T15:57:35.422411Z"
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@@ -4015,10 +4015,10 @@
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@@ -4033,10 +4033,10 @@
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@@ -5870,10 +5870,10 @@
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@@ -5890,10 +5890,10 @@
    "execution_count": 13,
    "metadata": {
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-     "shell.execute_reply": "2023-09-19T15:57:35.511621Z"
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@@ -5917,10 +5917,10 @@
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-     "shell.execute_reply": "2023-09-19T15:57:35.522606Z"
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@@ -5952,10 +5952,10 @@
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@@ -5992,10 +5992,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.537792Z",
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-     "iopub.status.idle": "2023-09-19T15:57:35.565400Z",
-     "shell.execute_reply": "2023-09-19T15:57:35.564521Z"
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@@ -6004,7 +6004,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MaxHR': 'numeric', 'Cholesterol': 'numeric', 'FastingBS': 'binary', 'ChestPainType': 'ordinal', 'Age': 'numeric', 'RestingBP': 'numeric', 'Sex': 'binary', 'RestingECG': 'ordinal', 'ST_Slope': 'ordinal', 'outcome': 'binary', 'ExerciseAngina': 'binary', 'Oldpeak': 'numeric'}\n"
+      "{'RestingECG': 'ordinal', 'MaxHR': 'numeric', 'Sex': 'binary', 'FastingBS': 'binary', 'outcome': 'binary', 'ExerciseAngina': 'binary', 'ChestPainType': 'ordinal', 'RestingBP': 'numeric', 'Cholesterol': 'numeric', 'Oldpeak': 'numeric', 'Age': 'numeric', 'ST_Slope': 'ordinal'}\n"
      ]
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@@ -6032,10 +6032,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.568872Z",
-     "iopub.status.busy": "2023-09-19T15:57:35.568335Z",
-     "iopub.status.idle": "2023-09-19T15:57:35.574429Z",
-     "shell.execute_reply": "2023-09-19T15:57:35.573475Z"
+     "iopub.execute_input": "2023-09-21T15:13:41.888241Z",
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@@ -6055,10 +6055,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-09-19T15:57:35.593763Z"
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@@ -6084,10 +6084,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2023-09-19T15:57:35.615499Z",
-     "shell.execute_reply": "2023-09-19T15:57:35.613889Z"
+     "iopub.execute_input": "2023-09-21T15:13:41.916904Z",
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@@ -6118,10 +6118,10 @@
    "execution_count": 20,
    "metadata": {
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@@ -6161,10 +6161,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.639427Z",
-     "iopub.status.busy": "2023-09-19T15:57:35.638620Z",
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-     "shell.execute_reply": "2023-09-19T15:57:35.748076Z"
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     }
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@@ -6213,10 +6213,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.757157Z",
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-     "shell.execute_reply": "2023-09-19T15:57:35.777070Z"
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@@ -6243,10 +6243,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.782316Z",
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     },
     "tags": []
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@@ -6264,7 +6264,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Casting the dataset: 100%|██████████| 918/918 [00:00<00:00, 172739.84 examples/s]"
+      "Casting the dataset: 100%|██████████| 918/918 [00:00<00:00, 187438.96 examples/s]"
      ]
     },
     {
@@ -6298,10 +6298,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:35.825222Z",
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-     "shell.execute_reply": "2023-09-19T15:57:36.667364Z"
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@@ -6325,10 +6325,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:36.671549Z",
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-     "shell.execute_reply": "2023-09-19T15:57:36.739703Z"
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@@ -6346,10 +6346,10 @@
    "execution_count": 26,
    "metadata": {
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-     "iopub.execute_input": "2023-09-19T15:57:36.744007Z",
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-     "shell.execute_reply": "2023-09-19T15:57:36.752058Z"
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@@ -6385,10 +6385,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:36.755568Z",
-     "iopub.status.busy": "2023-09-19T15:57:36.755354Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.370136Z"
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     },
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@@ -6397,21 +6397,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:42,363 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
+      "2023-09-21 11:13:48,423 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:42,364 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best eta0: 0.01\n"
+      "2023-09-21 11:13:48,424 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best eta0: 0.01\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:42,364 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: adaptive\n"
+      "2023-09-21 11:13:48,425 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: adaptive\n"
      ]
     },
     {
@@ -6456,10 +6456,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:42.373871Z",
-     "iopub.status.busy": "2023-09-19T15:57:42.373607Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.377139Z"
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     }
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@@ -6490,10 +6490,10 @@
    "execution_count": 29,
    "metadata": {
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-     "iopub.execute_input": "2023-09-19T15:57:42.381587Z",
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     }
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@@ -6515,10 +6515,10 @@
    "execution_count": 30,
    "metadata": {
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-     "iopub.execute_input": "2023-09-19T15:57:42.390276Z",
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     },
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@@ -6536,7 +6536,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|██████████| 184/184 [00:00<00:00, 8670.20 examples/s]"
+      "Map: 100%|██████████| 184/184 [00:00<00:00, 7778.74 examples/s]"
      ]
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     {
@@ -6581,10 +6581,10 @@
    "execution_count": 31,
    "metadata": {
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@@ -6615,10 +6615,10 @@
    "execution_count": 32,
    "metadata": {
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@@ -6657,10 +6657,10 @@
    "execution_count": 33,
    "metadata": {
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-     "iopub.execute_input": "2023-09-19T15:57:42.456341Z",
-     "iopub.status.busy": "2023-09-19T15:57:42.455918Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.461441Z"
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@@ -6701,10 +6701,10 @@
    "execution_count": 34,
    "metadata": {
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-     "shell.execute_reply": "2023-09-19T15:57:42.471633Z"
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     },
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@@ -6733,10 +6733,10 @@
    "execution_count": 35,
    "metadata": {
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-     "shell.execute_reply": "2023-09-19T15:57:42.770039Z"
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     },
     "tags": []
    },
@@ -6754,7 +6754,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|██████████| 184/184 [00:00<00:00, 7813.47 examples/s]"
+      "Map: 100%|██████████| 184/184 [00:00<00:00, 7153.93 examples/s]"
      ]
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     {
@@ -6777,7 +6777,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Flattening the indices: 100%|██████████| 184/184 [00:00<00:00, 8233.78 examples/s]"
      ]
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     {
@@ -6800,7 +6800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Flattening the indices: 100%|██████████| 184/184 [00:00<00:00, 48981.46 examples/s]"
      ]
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     {
@@ -6823,7 +6823,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Filter -> Age:[30 - 50): 100%|██████████| 184/184 [00:00<00:00, 19480.81 examples/s]"
      ]
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     {
@@ -6846,7 +6846,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Filter -> Age:[50 - 70): 100%|██████████| 184/184 [00:00<00:00, 15568.62 examples/s]"
      ]
     },
     {
@@ -6869,7 +6869,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Filter -> overall: 100%|██████████| 184/184 [00:00<00:00, 21303.81 examples/s]"
      ]
     },
     {
@@ -6892,7 +6892,7 @@
      "output_type": "stream",
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       "\r",
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+      "Filter -> Age:(-inf - 40.0]: 100%|██████████| 184/184 [00:00<00:00, 20113.94 examples/s]"
      ]
     },
     {
@@ -6915,7 +6915,7 @@
      "output_type": "stream",
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       "\r",
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      ]
     },
     {
@@ -6938,7 +6938,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Age:(50.0 - inf]: 100%|██████████| 184/184 [00:00<00:00, 20995.48 examples/s]"
+      "Filter -> Age:(50.0 - inf]: 100%|██████████| 184/184 [00:00<00:00, 20855.91 examples/s]"
      ]
     },
     {
@@ -6979,10 +6979,10 @@
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:42.773994Z",
-     "iopub.status.busy": "2023-09-19T15:57:42.773695Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.777326Z"
+     "iopub.execute_input": "2023-09-21T15:13:48.875457Z",
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     }
    },
    "outputs": [
@@ -7008,10 +7008,10 @@
    "execution_count": 37,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-09-19T15:57:42.790387Z"
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     }
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    "outputs": [],
@@ -7041,10 +7041,10 @@
    "execution_count": 38,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:42.794431Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.853120Z"
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     }
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    "outputs": [],
@@ -7058,10 +7058,10 @@
    "execution_count": 39,
    "metadata": {
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-     "iopub.status.busy": "2023-09-19T15:57:42.857312Z",
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-     "shell.execute_reply": "2023-09-19T15:57:42.862808Z"
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+     "shell.execute_reply": "2023-09-21T15:13:48.956805Z"
     }
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    "outputs": [
@@ -7094,10 +7094,10 @@
    "execution_count": 40,
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-     "shell.execute_reply": "2023-09-19T15:57:42.894332Z"
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+     "shell.execute_reply": "2023-09-21T15:13:48.992207Z"
     }
    },
    "outputs": [
@@ -8705,9 +8705,9 @@
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       },
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@@ -8773,10 +8773,10 @@
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@@ -9638,9 +9638,9 @@
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@@ -9688,10 +9688,10 @@
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-     "shell.execute_reply": "2023-09-19T15:57:42.946835Z"
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@@ -9712,10 +9712,10 @@
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@@ -10615,9 +10615,9 @@
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    "metadata": {
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@@ -10684,10 +10684,10 @@
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-     "iopub.execute_input": "2023-09-19T15:57:42.984434Z",
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-     "shell.execute_reply": "2023-09-19T15:57:43.008906Z"
+     "iopub.execute_input": "2023-09-21T15:13:49.087126Z",
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+     "shell.execute_reply": "2023-09-21T15:13:49.113528Z"
     }
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 </div>
 
@@ -620,7 +620,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2023-09-19
+              <b>Date:</b> 2023-09-21
             
           
           <br>
@@ -818,8 +818,8 @@ <h2>Model Parameters</h2>
   
     
     <div class="subcard">
-      <h3>Tol</h3>
-      0.001
+      <h3>Average</h3>
+      False
     </div>
     
   
@@ -827,8 +827,8 @@ <h3>Tol</h3>
   
     
     <div class="subcard">
-      <h3>Loss</h3>
-      log_loss
+      <h3>Class_weight</h3>
+      balanced
     </div>
     
   
@@ -836,8 +836,8 @@ <h3>Loss</h3>
   
     
     <div class="subcard">
-      <h3>N_iter_no_change</h3>
-      5
+      <h3>Learning_rate</h3>
+      adaptive
     </div>
     
   
@@ -845,8 +845,8 @@ <h3>N_iter_no_change</h3>
   
     
     <div class="subcard">
-      <h3>Verbose</h3>
-      0
+      <h3>Fit_intercept</h3>
+      True
     </div>
     
   
@@ -854,8 +854,8 @@ <h3>Verbose</h3>
   
     
     <div class="subcard">
-      <h3>Power_t</h3>
-      0.5
+      <h3>Warm_start</h3>
+      False
     </div>
     
   
@@ -863,8 +863,8 @@ <h3>Power_t</h3>
   
     
     <div class="subcard">
-      <h3>Warm_start</h3>
-      False
+      <h3>Shuffle</h3>
+      True
     </div>
     
   
@@ -872,8 +872,8 @@ <h3>Warm_start</h3>
   
     
     <div class="subcard">
-      <h3>Validation_fraction</h3>
-      0.1
+      <h3>Power_t</h3>
+      0.5
     </div>
     
   
@@ -881,8 +881,8 @@ <h3>Validation_fraction</h3>
   
     
     <div class="subcard">
-      <h3>Eta0</h3>
-      0.01
+      <h3>Alpha</h3>
+      0.001
     </div>
     
   
@@ -890,26 +890,22 @@ <h3>Eta0</h3>
   
     
     <div class="subcard">
-      <h3>L1_ratio</h3>
-      0.15
+      <h3>Epsilon</h3>
+      0.1
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Early_stopping</h3>
-      True
-    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Learning_rate</h3>
-      adaptive
+      <h3>Eta0</h3>
+      0.01
     </div>
     
   
@@ -917,8 +913,8 @@ <h3>Learning_rate</h3>
   
     
     <div class="subcard">
-      <h3>Alpha</h3>
-      0.001
+      <h3>L1_ratio</h3>
+      0.15
     </div>
     
   
@@ -926,8 +922,8 @@ <h3>Alpha</h3>
   
     
     <div class="subcard">
-      <h3>Fit_intercept</h3>
-      True
+      <h3>Penalty</h3>
+      l2
     </div>
     
   
@@ -935,8 +931,8 @@ <h3>Fit_intercept</h3>
   
     
     <div class="subcard">
-      <h3>Penalty</h3>
-      l2
+      <h3>Random_state</h3>
+      123
     </div>
     
   
@@ -944,14 +940,18 @@ <h3>Penalty</h3>
   
     
     <div class="subcard">
-      <h3>Average</h3>
-      False
+      <h3>Validation_fraction</h3>
+      0.1
     </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Verbose</h3>
+      0
+    </div>
     
   
 
@@ -967,8 +967,8 @@ <h3>Max_iter</h3>
   
     
     <div class="subcard">
-      <h3>Random_state</h3>
-      123
+      <h3>Loss</h3>
+      log_loss
     </div>
     
   
@@ -976,8 +976,8 @@ <h3>Random_state</h3>
   
     
     <div class="subcard">
-      <h3>Epsilon</h3>
-      0.1
+      <h3>N_iter_no_change</h3>
+      5
     </div>
     
   
@@ -985,8 +985,8 @@ <h3>Epsilon</h3>
   
     
     <div class="subcard">
-      <h3>Class_weight</h3>
-      balanced
+      <h3>Tol</h3>
+      0.001
     </div>
     
   
@@ -994,7 +994,7 @@ <h3>Class_weight</h3>
   
     
     <div class="subcard">
-      <h3>Shuffle</h3>
+      <h3>Early_stopping</h3>
       True
     </div>
     
@@ -1404,7 +1404,7 @@ <h3>Graphics</h3>
     
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diff --git a/api/tutorials/mimiciii/query_api.html b/api/tutorials/mimiciii/query_api.html
index d57714f06..2fb79c10b 100644
--- a/api/tutorials/mimiciii/query_api.html
+++ b/api/tutorials/mimiciii/query_api.html
@@ -483,9 +483,9 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-19 11:57:55,789 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:14:04,262 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -519,8 +519,8 @@ <h2>Example 1. Get all male patients with a mortality outcome.<a class="headerli
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:59,671 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:59,673 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.035064 s
+2023-09-21 11:14:11,656 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:14:11,657 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.037987 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -560,8 +560,8 @@ <h2>Example 2. Get all female patient encounters with diagnoses (<code class="do
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:59,807 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:59,808 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.100985 s
+2023-09-21 11:14:11,795 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:14:11,796 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.096709 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -593,8 +593,8 @@ <h2>Example 3. Get potassium lab tests for female patients.<a class="headerlink"
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:57:59,862 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:57:59,864 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.033763 s
+2023-09-21 11:14:11,865 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:14:11,866 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.032713 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -631,8 +631,8 @@ <h2>Example 4. Get AaDO2 carevue chart events for male patients that have a <cod
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:59:09,167 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:59:09,168 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 69.262418 s
+2023-09-21 11:15:24,022 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:15:24,023 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 72.113653 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/api/tutorials/mimiciii/query_api.ipynb b/api/tutorials/mimiciii/query_api.ipynb
index c66ba289b..f2d2ac74b 100644
--- a/api/tutorials/mimiciii/query_api.ipynb
+++ b/api/tutorials/mimiciii/query_api.ipynb
@@ -35,10 +35,10 @@
    "id": "75a140e0-fb27-4319-862f-be54397abe5c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:50.344754Z",
-     "iopub.status.busy": "2023-09-19T15:57:50.344044Z",
-     "iopub.status.idle": "2023-09-19T15:57:59.621343Z",
-     "shell.execute_reply": "2023-09-19T15:57:59.619328Z"
+     "iopub.execute_input": "2023-09-21T15:13:56.082278Z",
+     "iopub.status.busy": "2023-09-21T15:13:56.081599Z",
+     "iopub.status.idle": "2023-09-21T15:14:11.604495Z",
+     "shell.execute_reply": "2023-09-21T15:14:11.602826Z"
     },
     "tags": []
    },
@@ -47,7 +47,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -55,7 +55,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:55,789 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:14:04,262 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
@@ -102,10 +102,10 @@
    "id": "c1efa964-8978-4a0e-9892-5ea4ce9953a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:59.630871Z",
-     "iopub.status.busy": "2023-09-19T15:57:59.629812Z",
-     "iopub.status.idle": "2023-09-19T15:57:59.680955Z",
-     "shell.execute_reply": "2023-09-19T15:57:59.679605Z"
+     "iopub.execute_input": "2023-09-21T15:14:11.611824Z",
+     "iopub.status.busy": "2023-09-21T15:14:11.611095Z",
+     "iopub.status.idle": "2023-09-21T15:14:11.663843Z",
+     "shell.execute_reply": "2023-09-21T15:14:11.662029Z"
     },
     "tags": []
    },
@@ -114,14 +114,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,671 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:14:11,656 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,673 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.035064 s\n"
+      "2023-09-21 11:14:11,657 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.037987 s\n"
      ]
     },
     {
@@ -158,10 +158,10 @@
    "id": "a7ab5fa3-e26b-47a7-818f-1bf367a55760",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:59.685038Z",
-     "iopub.status.busy": "2023-09-19T15:57:59.684574Z",
-     "iopub.status.idle": "2023-09-19T15:57:59.813260Z",
-     "shell.execute_reply": "2023-09-19T15:57:59.812131Z"
+     "iopub.execute_input": "2023-09-21T15:14:11.670802Z",
+     "iopub.status.busy": "2023-09-21T15:14:11.670277Z",
+     "iopub.status.idle": "2023-09-21T15:14:11.803055Z",
+     "shell.execute_reply": "2023-09-21T15:14:11.801537Z"
     },
     "tags": []
    },
@@ -170,14 +170,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,807 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:14:11,795 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,808 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.100985 s\n"
+      "2023-09-21 11:14:11,796 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.096709 s\n"
      ]
     },
     {
@@ -219,10 +219,10 @@
    "id": "24043abc-1878-4e00-8229-36d4a0368b98",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:57:59.818530Z",
-     "iopub.status.busy": "2023-09-19T15:57:59.818186Z",
-     "iopub.status.idle": "2023-09-19T15:57:59.870398Z",
-     "shell.execute_reply": "2023-09-19T15:57:59.866909Z"
+     "iopub.execute_input": "2023-09-21T15:14:11.809563Z",
+     "iopub.status.busy": "2023-09-21T15:14:11.808863Z",
+     "iopub.status.idle": "2023-09-21T15:14:11.871782Z",
+     "shell.execute_reply": "2023-09-21T15:14:11.870700Z"
     },
     "tags": []
    },
@@ -231,14 +231,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,862 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:14:11,865 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:57:59,864 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.033763 s\n"
+      "2023-09-21 11:14:11,866 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.032713 s\n"
      ]
     },
     {
@@ -272,10 +272,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2023-09-19T15:59:09.172192Z",
-     "shell.execute_reply": "2023-09-19T15:59:09.171392Z"
+     "iopub.execute_input": "2023-09-21T15:14:11.879018Z",
+     "iopub.status.busy": "2023-09-21T15:14:11.878318Z",
+     "iopub.status.idle": "2023-09-21T15:15:24.026868Z",
+     "shell.execute_reply": "2023-09-21T15:15:24.026125Z"
     },
     "tags": []
    },
@@ -284,14 +284,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:09,167 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:15:24,022 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:09,168 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 69.262418 s\n"
+      "2023-09-21 11:15:24,023 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 72.113653 s\n"
      ]
     },
     {
diff --git a/api/tutorials/mimiciv/query_api.html b/api/tutorials/mimiciv/query_api.html
index 5d003bf59..5d505e229 100644
--- a/api/tutorials/mimiciv/query_api.html
+++ b/api/tutorials/mimiciv/query_api.html
@@ -487,9 +487,9 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-19 11:59:15,401 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:15:29,005 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -534,8 +534,8 @@ <h2>Example 1. Get all patient admissions from 2021 or later (approx year of adm
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:59:17,601 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:59:17,602 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.182561 s
+2023-09-21 11:15:30,728 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:15:30,729 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.269253 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -580,8 +580,8 @@ <h2>Example 2. Get all patient encounters with diagnoses (<code class="docutils
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:59:18,400 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:59:18,400 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.760419 s
+2023-09-21 11:15:31,581 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:15:31,582 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.819763 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -626,8 +626,8 @@ <h2>Example 3. Advanced - uses <code class="docutils literal notranslate"><span
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 11:59:20,036 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 11:59:20,037 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 1.526170 s
+2023-09-21 11:15:33,139 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:15:33,140 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 1.516252 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -668,8 +668,8 @@ <h2>Example 4. Get routine vital signs for patients from year 2015.<a class="hea
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:00:38,995 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:00:38,997 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 78.914878 s
+2023-09-21 11:16:55,506 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:16:55,507 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 82.326835 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -710,8 +710,8 @@ <h2>Example 5. Get hemoglobin lab tests for patients from year 2009.<a class="he
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:01:47,563 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:01:47,564 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 68.524319 s
+2023-09-21 11:18:06,785 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:18:06,786 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 71.250068 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -758,8 +758,8 @@ <h2>Example 6. Get radiology reports and filter on keywords <code class="docutil
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:01:54,873 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:01:54,874 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 7.285099 s
+2023-09-21 11:18:14,480 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:18:14,482 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 7.654839 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -803,8 +803,8 @@ <h2>Example 7. Get all female patient encounters from year 2015, and return as d
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:01:55,290 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:01:55,291 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.383649 s
+2023-09-21 11:18:14,852 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:18:14,853 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.330419 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -834,8 +834,8 @@ <h2>Example 8. Running a raw SQL string.<a class="headerlink" href="#Example-8.-
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:01:56,271 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:01:56,272 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.007934 s
+2023-09-21 11:18:15,647 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:18:15,648 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.009600 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/api/tutorials/mimiciv/query_api.ipynb b/api/tutorials/mimiciv/query_api.ipynb
index d0ca847c8..3690e92e8 100644
--- a/api/tutorials/mimiciv/query_api.ipynb
+++ b/api/tutorials/mimiciv/query_api.ipynb
@@ -35,10 +35,10 @@
    "id": "53009e6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:59:12.297843Z",
-     "iopub.status.busy": "2023-09-19T15:59:12.297133Z",
-     "iopub.status.idle": "2023-09-19T15:59:17.396087Z",
-     "shell.execute_reply": "2023-09-19T15:59:17.394836Z"
+     "iopub.execute_input": "2023-09-21T15:15:26.614909Z",
+     "iopub.status.busy": "2023-09-21T15:15:26.614291Z",
+     "iopub.status.idle": "2023-09-21T15:15:30.431295Z",
+     "shell.execute_reply": "2023-09-21T15:15:30.429985Z"
     },
     "tags": []
    },
@@ -47,7 +47,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -55,7 +55,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:15,401 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:15:29,005 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
@@ -113,10 +113,10 @@
    "id": "cdfadaa4-6fd6-4fd7-85cf-e012aa0799e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:59:17.403117Z",
-     "iopub.status.busy": "2023-09-19T15:59:17.402587Z",
-     "iopub.status.idle": "2023-09-19T15:59:17.606050Z",
-     "shell.execute_reply": "2023-09-19T15:59:17.604903Z"
+     "iopub.execute_input": "2023-09-21T15:15:30.437531Z",
+     "iopub.status.busy": "2023-09-21T15:15:30.436712Z",
+     "iopub.status.idle": "2023-09-21T15:15:30.731938Z",
+     "shell.execute_reply": "2023-09-21T15:15:30.731372Z"
     }
    },
    "outputs": [
@@ -124,14 +124,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:17,601 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:15:30,728 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:17,602 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.182561 s\n"
+      "2023-09-21 11:15:30,729 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.269253 s\n"
      ]
     },
     {
@@ -168,10 +168,10 @@
    "id": "a89a9cf0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:59:17.617522Z",
-     "iopub.status.busy": "2023-09-19T15:59:17.616611Z",
-     "iopub.status.idle": "2023-09-19T15:59:18.403611Z",
-     "shell.execute_reply": "2023-09-19T15:59:18.402960Z"
+     "iopub.execute_input": "2023-09-21T15:15:30.737267Z",
+     "iopub.status.busy": "2023-09-21T15:15:30.736904Z",
+     "iopub.status.idle": "2023-09-21T15:15:31.588167Z",
+     "shell.execute_reply": "2023-09-21T15:15:31.586916Z"
     }
    },
    "outputs": [
@@ -179,14 +179,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:18,400 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:15:31,581 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:18,400 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.760419 s\n"
+      "2023-09-21 11:15:31,582 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.819763 s\n"
      ]
     },
     {
@@ -233,10 +233,10 @@
    "id": "03936cee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:59:18.406763Z",
-     "iopub.status.busy": "2023-09-19T15:59:18.406455Z",
-     "iopub.status.idle": "2023-09-19T15:59:20.042003Z",
-     "shell.execute_reply": "2023-09-19T15:59:20.040692Z"
+     "iopub.execute_input": "2023-09-21T15:15:31.592844Z",
+     "iopub.status.busy": "2023-09-21T15:15:31.592045Z",
+     "iopub.status.idle": "2023-09-21T15:15:33.143620Z",
+     "shell.execute_reply": "2023-09-21T15:15:33.142769Z"
     }
    },
    "outputs": [
@@ -244,14 +244,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:20,036 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:15:33,139 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 11:59:20,037 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.526170 s\n"
+      "2023-09-21 11:15:33,140 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.516252 s\n"
      ]
     },
     {
@@ -298,10 +298,10 @@
    "id": "56a72377",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T15:59:20.048724Z",
-     "iopub.status.busy": "2023-09-19T15:59:20.048150Z",
-     "iopub.status.idle": "2023-09-19T16:00:39.003143Z",
-     "shell.execute_reply": "2023-09-19T16:00:39.001738Z"
+     "iopub.execute_input": "2023-09-21T15:15:33.151407Z",
+     "iopub.status.busy": "2023-09-21T15:15:33.150642Z",
+     "iopub.status.idle": "2023-09-21T15:16:55.511076Z",
+     "shell.execute_reply": "2023-09-21T15:16:55.510453Z"
     }
    },
    "outputs": [
@@ -309,14 +309,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:00:38,995 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:16:55,506 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:00:38,997 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 78.914878 s\n"
+      "2023-09-21 11:16:55,507 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 82.326835 s\n"
      ]
     },
     {
@@ -359,10 +359,10 @@
    "id": "bce11f81",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:00:39.009283Z",
-     "iopub.status.busy": "2023-09-19T16:00:39.008569Z",
-     "iopub.status.idle": "2023-09-19T16:01:47.567689Z",
-     "shell.execute_reply": "2023-09-19T16:01:47.566707Z"
+     "iopub.execute_input": "2023-09-21T15:16:55.518731Z",
+     "iopub.status.busy": "2023-09-21T15:16:55.518490Z",
+     "iopub.status.idle": "2023-09-21T15:18:06.790720Z",
+     "shell.execute_reply": "2023-09-21T15:18:06.789736Z"
     }
    },
    "outputs": [
@@ -370,14 +370,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:47,563 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:18:06,785 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:47,564 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 68.524319 s\n"
+      "2023-09-21 11:18:06,786 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 71.250068 s\n"
      ]
     },
     {
@@ -420,10 +420,10 @@
    "id": "f00d270c-d78f-4dc0-8dae-ff4d52958c8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:01:47.575674Z",
-     "iopub.status.busy": "2023-09-19T16:01:47.575269Z",
-     "iopub.status.idle": "2023-09-19T16:01:54.877348Z",
-     "shell.execute_reply": "2023-09-19T16:01:54.876661Z"
+     "iopub.execute_input": "2023-09-21T15:18:06.795571Z",
+     "iopub.status.busy": "2023-09-21T15:18:06.795175Z",
+     "iopub.status.idle": "2023-09-21T15:18:14.487593Z",
+     "shell.execute_reply": "2023-09-21T15:18:14.486250Z"
     },
     "tags": []
    },
@@ -432,14 +432,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:54,873 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:18:14,480 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:54,874 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 7.285099 s\n"
+      "2023-09-21 11:18:14,482 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 7.654839 s\n"
      ]
     },
     {
@@ -488,10 +488,10 @@
    "id": "28683d70-376e-4d9b-883d-1a7de634e455",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:01:54.885166Z",
-     "iopub.status.busy": "2023-09-19T16:01:54.884793Z",
-     "iopub.status.idle": "2023-09-19T16:01:56.252838Z",
-     "shell.execute_reply": "2023-09-19T16:01:56.251050Z"
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    },
    "outputs": [
@@ -499,14 +499,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:55,290 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:18:14,852 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
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     {
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-      "2023-09-19 12:01:55,291 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.383649 s\n"
+      "2023-09-21 11:18:14,853 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.330419 s\n"
      ]
     },
     {
@@ -554,10 +554,10 @@
    "id": "a853deec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:01:56.262925Z",
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-     "iopub.status.idle": "2023-09-19T16:01:56.277565Z",
-     "shell.execute_reply": "2023-09-19T16:01:56.276252Z"
+     "iopub.execute_input": "2023-09-21T15:18:15.636606Z",
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+     "iopub.status.idle": "2023-09-21T15:18:15.653563Z",
+     "shell.execute_reply": "2023-09-21T15:18:15.652154Z"
     },
     "tags": []
    },
@@ -566,14 +566,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:56,271 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:18:15,647 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:01:56,272 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.007934 s\n"
+      "2023-09-21 11:18:15,648 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.009600 s\n"
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diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html
index 190b0f400..f095df7b1 100644
--- a/api/tutorials/nihcxr/cxr_classification.html
+++ b/api/tutorials/nihcxr/cxr_classification.html
@@ -488,7 +488,7 @@ <h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link t
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 </pre></div></div>
 </div>
@@ -574,8 +574,8 @@ <h2>Load Model and get Predictions<a class="headerlink" href="#Load-Model-and-ge
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Filter: 100%|██████████| 4000/4000 [00:00&lt;00:00, 296957.64 examples/s]
-Map: 100%|██████████| 2511/2511 [00:00&lt;00:00, 3395.27 examples/s]
+Filter: 100%|██████████| 4000/4000 [00:00&lt;00:00, 212857.51 examples/s]
+Map: 100%|██████████| 2511/2511 [00:00&lt;00:00, 3764.42 examples/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -664,9 +664,9 @@ <h2>Multilabel AUROC by Pathology and Sex<a class="headerlink" href="#Multilabel
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Filter -&gt; Patient Gender:M: 100%|██████████| 2511/2511 [00:00&lt;00:00, 58922.67 examples/s]
-Filter -&gt; Patient Gender:F: 100%|██████████| 2511/2511 [00:00&lt;00:00, 59648.84 examples/s]
-Filter -&gt; overall: 100%|██████████| 2511/2511 [00:00&lt;00:00, 63840.90 examples/s]
+Filter -&gt; Patient Gender:M: 100%|██████████| 2511/2511 [00:00&lt;00:00, 59679.94 examples/s]
+Filter -&gt; Patient Gender:F: 100%|██████████| 2511/2511 [00:00&lt;00:00, 60636.52 examples/s]
+Filter -&gt; overall: 100%|██████████| 2511/2511 [00:00&lt;00:00, 60682.64 examples/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -698,9 +698,9 @@ <h2>Multilabel AUROC by Pathology and Sex<a class="headerlink" href="#Multilabel
 <div class="prompt empty docutils container">
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-Filter -&gt; Patient Age:[19 - 35]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 60595.71 examples/s]
-Filter -&gt; Patient Age:[35 - 65]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 61452.05 examples/s]
-Filter -&gt; Patient Age:[65 - 100]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 52358.43 examples/s]
-Filter -&gt; overall: 100%|██████████| 2511/2511 [00:00&lt;00:00, 49193.78 examples/s]
+Filter -&gt; Patient Age:[19 - 35]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 63385.98 examples/s]
+Filter -&gt; Patient Age:[35 - 65]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 61383.28 examples/s]
+Filter -&gt; Patient Age:[65 - 100]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 61635.46 examples/s]
+Filter -&gt; overall: 100%|██████████| 2511/2511 [00:00&lt;00:00, 62553.96 examples/s]
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+Filter -&gt; Patient Age:(65.0 - inf]: 100%|██████████| 2511/2511 [00:00&lt;00:00, 61603.73 examples/s]
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diff --git a/api/tutorials/nihcxr/cxr_classification.ipynb b/api/tutorials/nihcxr/cxr_classification.ipynb
index 1d96e08e1..7be1e0606 100644
--- a/api/tutorials/nihcxr/cxr_classification.ipynb
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@@ -172,10 +172,10 @@
    "id": "5f624ed4",
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-     "iopub.status.idle": "2023-09-19T16:03:12.416549Z",
-     "shell.execute_reply": "2023-09-19T16:03:12.415775Z"
+     "iopub.execute_input": "2023-09-21T15:18:27.104998Z",
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+     "iopub.status.idle": "2023-09-21T15:19:31.521093Z",
+     "shell.execute_reply": "2023-09-21T15:19:31.520153Z"
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    "outputs": [
@@ -192,7 +192,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter: 100%|██████████| 4000/4000 [00:00<00:00, 296957.64 examples/s]"
+      "Filter: 100%|██████████| 4000/4000 [00:00<00:00, 212857.51 examples/s]"
      ]
     },
     {
@@ -215,7 +215,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map:  23%|██▎       | 583/2511 [00:00<00:00, 5530.98 examples/s]"
+      "Map:  27%|██▋       | 669/2511 [00:00<00:00, 6559.01 examples/s]"
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     {
@@ -223,7 +223,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map:  52%|█████▏    | 1312/2511 [00:00<00:00, 3412.11 examples/s]"
+      "Map:  67%|██████▋   | 1681/2511 [00:00<00:00, 4364.57 examples/s]"
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     {
@@ -231,7 +231,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map:  80%|███████▉  | 2000/2511 [00:00<00:00, 3198.64 examples/s]"
+      "Map:  92%|█████████▏| 2315/2511 [00:00<00:00, 3735.49 examples/s]"
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     {
@@ -239,15 +239,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|██████████| 2511/2511 [00:00<00:00, 3349.67 examples/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Map: 100%|██████████| 2511/2511 [00:00<00:00, 3395.27 examples/s]"
+      "Map: 100%|██████████| 2511/2511 [00:00<00:00, 3764.42 examples/s]"
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     {
@@ -305,10 +297,10 @@
    "id": "bff27cc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:12.424334Z",
-     "iopub.status.busy": "2023-09-19T16:03:12.424094Z",
-     "iopub.status.idle": "2023-09-19T16:03:13.263359Z",
-     "shell.execute_reply": "2023-09-19T16:03:13.262635Z"
+     "iopub.execute_input": "2023-09-21T15:19:31.530596Z",
+     "iopub.status.busy": "2023-09-21T15:19:31.530340Z",
+     "iopub.status.idle": "2023-09-21T15:19:32.377276Z",
+     "shell.execute_reply": "2023-09-21T15:19:32.376594Z"
     }
    },
    "outputs": [
@@ -325,7 +317,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:M: 100%|██████████| 2511/2511 [00:00<00:00, 58922.67 examples/s]"
+      "Filter -> Patient Gender:M: 100%|██████████| 2511/2511 [00:00<00:00, 59679.94 examples/s]"
      ]
     },
     {
@@ -348,7 +340,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:F: 100%|██████████| 2511/2511 [00:00<00:00, 59648.84 examples/s]"
+      "Filter -> Patient Gender:F: 100%|██████████| 2511/2511 [00:00<00:00, 60636.52 examples/s]"
      ]
     },
     {
@@ -371,7 +363,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 63840.90 examples/s]"
+      "Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 60682.64 examples/s]"
      ]
     },
     {
@@ -1386,9 +1378,9 @@
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        "                            \n",
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+       "var gd = document.getElementById('e5f0ec98-7aec-4852-98b8-960c9fbac061');\n",
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@@ -1489,10 +1481,10 @@
    "id": "8c38ef9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:13.313010Z",
-     "iopub.status.busy": "2023-09-19T16:03:13.312378Z",
-     "iopub.status.idle": "2023-09-19T16:03:14.174377Z",
-     "shell.execute_reply": "2023-09-19T16:03:14.173716Z"
+     "iopub.execute_input": "2023-09-21T15:19:32.429561Z",
+     "iopub.status.busy": "2023-09-21T15:19:32.429050Z",
+     "iopub.status.idle": "2023-09-21T15:19:33.203401Z",
+     "shell.execute_reply": "2023-09-21T15:19:33.202565Z"
     }
    },
    "outputs": [
@@ -1509,7 +1501,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[19 - 35]: 100%|██████████| 2511/2511 [00:00<00:00, 60595.71 examples/s]"
+      "Filter -> Patient Age:[19 - 35]: 100%|██████████| 2511/2511 [00:00<00:00, 63385.98 examples/s]"
      ]
     },
     {
@@ -1532,7 +1524,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[35 - 65]: 100%|██████████| 2511/2511 [00:00<00:00, 61452.05 examples/s]"
+      "Filter -> Patient Age:[35 - 65]: 100%|██████████| 2511/2511 [00:00<00:00, 61383.28 examples/s]"
      ]
     },
     {
@@ -1555,7 +1547,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[65 - 100]: 100%|██████████| 2511/2511 [00:00<00:00, 52358.43 examples/s]"
+      "Filter -> Patient Age:[65 - 100]: 100%|██████████| 2511/2511 [00:00<00:00, 61635.46 examples/s]"
      ]
     },
     {
@@ -1578,7 +1570,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 49193.78 examples/s]"
+      "Filter -> overall: 100%|██████████| 2511/2511 [00:00<00:00, 62553.96 examples/s]"
      ]
     },
     {
@@ -2607,9 +2599,9 @@
        }
       },
       "text/html": [
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@@ -2702,10 +2694,10 @@
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-     "iopub.status.idle": "2023-09-19T16:03:14.543100Z",
-     "shell.execute_reply": "2023-09-19T16:03:14.542466Z"
+     "iopub.execute_input": "2023-09-21T15:19:33.211439Z",
+     "iopub.status.busy": "2023-09-21T15:19:33.211243Z",
+     "iopub.status.idle": "2023-09-21T15:19:33.626918Z",
+     "shell.execute_reply": "2023-09-21T15:19:33.626128Z"
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@@ -3625,10 +3617,10 @@
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-     "shell.execute_reply": "2023-09-19T16:03:14.603280Z"
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+     "iopub.status.idle": "2023-09-21T15:19:33.715273Z",
+     "shell.execute_reply": "2023-09-21T15:19:33.714428Z"
     }
    },
    "outputs": [
@@ -7026,9 +7018,9 @@
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@@ -7082,10 +7074,10 @@
    "id": "d2ff6506",
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-     "iopub.status.idle": "2023-09-19T16:03:14.674131Z",
-     "shell.execute_reply": "2023-09-19T16:03:14.673091Z"
+     "iopub.execute_input": "2023-09-21T15:19:33.727182Z",
+     "iopub.status.busy": "2023-09-21T15:19:33.726804Z",
+     "iopub.status.idle": "2023-09-21T15:19:33.789018Z",
+     "shell.execute_reply": "2023-09-21T15:19:33.788245Z"
     }
    },
    "outputs": [
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@@ -8062,10 +8054,10 @@
    "id": "d1c587c1",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2023-09-19T16:03:14.678294Z",
-     "iopub.status.idle": "2023-09-19T16:03:15.177939Z",
-     "shell.execute_reply": "2023-09-19T16:03:15.176822Z"
+     "iopub.execute_input": "2023-09-21T15:19:33.797322Z",
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+     "shell.execute_reply": "2023-09-21T15:19:34.251336Z"
     }
    },
    "outputs": [
@@ -8082,7 +8074,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:(-inf - 35.0]: 100%|██████████| 2511/2511 [00:00<00:00, 61286.12 examples/s]"
+      "Filter -> Patient Age:(-inf - 35.0]: 100%|██████████| 2511/2511 [00:00<00:00, 62305.65 examples/s]"
      ]
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     {
@@ -8105,7 +8097,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:(35.0 - 65.0]: 100%|██████████| 2511/2511 [00:00<00:00, 60853.75 examples/s]"
+      "Filter -> Patient Age:(35.0 - 65.0]: 100%|██████████| 2511/2511 [00:00<00:00, 57153.77 examples/s]"
      ]
     },
     {
@@ -8128,7 +8120,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:(65.0 - inf]: 100%|██████████| 2511/2511 [00:00<00:00, 59483.09 examples/s]"
+      "Filter -> Patient Age:(65.0 - inf]: 100%|██████████| 2511/2511 [00:00<00:00, 61603.73 examples/s]"
      ]
     },
     {
@@ -9114,9 +9106,9 @@
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@@ -9216,10 +9208,10 @@
    "id": "2c44b0c4",
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     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:15.185865Z",
-     "iopub.status.busy": "2023-09-19T16:03:15.185599Z",
-     "iopub.status.idle": "2023-09-19T16:03:15.205198Z",
-     "shell.execute_reply": "2023-09-19T16:03:15.204620Z"
+     "iopub.execute_input": "2023-09-21T15:19:34.256570Z",
+     "iopub.status.busy": "2023-09-21T15:19:34.255840Z",
+     "iopub.status.idle": "2023-09-21T15:19:34.275863Z",
+     "shell.execute_reply": "2023-09-21T15:19:34.274906Z"
     }
    },
    "outputs": [
@@ -10199,9 +10191,9 @@
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@@ -10274,10 +10266,10 @@
    "id": "ec627997",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:15.216698Z",
-     "iopub.status.busy": "2023-09-19T16:03:15.216266Z",
-     "iopub.status.idle": "2023-09-19T16:03:15.257782Z",
-     "shell.execute_reply": "2023-09-19T16:03:15.256925Z"
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     }
    },
    "outputs": [],
@@ -10324,10 +10316,10 @@
    "id": "8db232df",
    "metadata": {
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-     "iopub.status.idle": "2023-09-19T16:03:15.758532Z",
-     "shell.execute_reply": "2023-09-19T16:03:15.757898Z"
+     "iopub.execute_input": "2023-09-21T15:19:34.318217Z",
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+     "shell.execute_reply": "2023-09-21T15:19:34.711525Z"
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@@ -10348,16 +10340,16 @@
          "showlegend": true,
          "type": "scatter",
          "x": [
-          "2022-03-19",
-          "2022-09-19",
-          "2023-03-19",
-          "2023-09-19"
+          "2022-03-21",
+          "2022-09-21",
+          "2023-03-21",
+          "2023-09-21"
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          "xaxis": "x",
          "y": [
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-          0.5665189114054356,
-          0.6869983119755999,
+          0.7147107528643464,
+          0.5839226407142346,
+          0.6820774115267161,
           0.6189784358659702
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          "yaxis": "y"
@@ -11206,10 +11198,10 @@
           1.0
          ],
          "tickvals": [
-          "2022-03-19",
-          "2022-09-19",
-          "2023-03-19",
-          "2023-09-19"
+          "2022-03-21",
+          "2022-09-21",
+          "2023-03-21",
+          "2023-09-21"
          ],
          "title": {
           "text": "Time Step"
@@ -11228,9 +11220,9 @@
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       },
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diff --git a/api/tutorials/nihcxr/model_card.html b/api/tutorials/nihcxr/model_card.html
index 10e6b31f8..c56fc6091 100644
--- a/api/tutorials/nihcxr/model_card.html
+++ b/api/tutorials/nihcxr/model_card.html
@@ -362,7 +362,7 @@ <h3>Test Summary</h3>
         
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@@ -1858,7 +1858,7 @@ <h3>Performance Over Time</h3>
             
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 </div>
 
diff --git a/api/tutorials/nihcxr/monitor_api.html b/api/tutorials/nihcxr/monitor_api.html
index 730d3cbcb..13790a09b 100644
--- a/api/tutorials/nihcxr/monitor_api.html
+++ b/api/tutorials/nihcxr/monitor_api.html
@@ -472,7 +472,7 @@ <h2>Import Libraries and Load NIHCXR Dataset<a class="headerlink" href="#Import-
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 </pre></div></div>
 </div>
@@ -517,7 +517,7 @@ <h2>Example 1. Generate Source/Target Dataset for Experiments (1-2)<a class="hea
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 69164.58 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 59651.02 examples/s]
 </pre></div></div>
 </div>
 </section>
@@ -663,14 +663,14 @@ <h2>Example 4. Sensitivity test experiment with different clinical shifts<a clas
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 48615.43 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 45453.10 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 41277.97 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47280.29 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 48170.42 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47720.96 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 46885.12 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47642.71 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 50082.92 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 49186.86 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47503.36 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47554.34 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47736.93 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 45598.12 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 45676.58 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00&lt;00:00, 47465.24 examples/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb
index 686d26649..3d915f820 100644
--- a/api/tutorials/nihcxr/monitor_api.ipynb
+++ b/api/tutorials/nihcxr/monitor_api.ipynb
@@ -22,10 +22,10 @@
    "id": "8aa3302d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:20.792312Z",
-     "iopub.status.busy": "2023-09-19T16:03:20.791692Z",
-     "iopub.status.idle": "2023-09-19T16:03:28.169902Z",
-     "shell.execute_reply": "2023-09-19T16:03:28.169066Z"
+     "iopub.execute_input": "2023-09-21T15:19:40.741494Z",
+     "iopub.status.busy": "2023-09-21T15:19:40.740965Z",
+     "iopub.status.idle": "2023-09-21T15:19:47.826976Z",
+     "shell.execute_reply": "2023-09-21T15:19:47.826290Z"
     }
    },
    "outputs": [
@@ -33,7 +33,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
@@ -69,10 +69,10 @@
    "id": "e11920db",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:28.175561Z",
-     "iopub.status.busy": "2023-09-19T16:03:28.175222Z",
-     "iopub.status.idle": "2023-09-19T16:03:28.702465Z",
-     "shell.execute_reply": "2023-09-19T16:03:28.701458Z"
+     "iopub.execute_input": "2023-09-21T15:19:47.831059Z",
+     "iopub.status.busy": "2023-09-21T15:19:47.830850Z",
+     "iopub.status.idle": "2023-09-21T15:19:48.421859Z",
+     "shell.execute_reply": "2023-09-21T15:19:48.420755Z"
     }
    },
    "outputs": [
@@ -89,7 +89,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6):  17%|█▋        | 4266/25596 [00:00<00:00, 30999.64 examples/s]"
+      "Filter (num_proc=6):  17%|█▋        | 4266/25596 [00:00<00:00, 33676.51 examples/s]"
      ]
     },
     {
@@ -97,7 +97,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6):  83%|████████▎ | 21330/25596 [00:00<00:00, 96964.51 examples/s]"
+      "Filter (num_proc=6):  67%|██████▋   | 17064/25596 [00:00<00:00, 74589.12 examples/s]"
      ]
     },
     {
@@ -105,7 +105,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 69164.58 examples/s]"
+      "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 59651.02 examples/s]"
      ]
     },
     {
@@ -159,16 +159,16 @@
    "id": "54a3523a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:28.707856Z",
-     "iopub.status.busy": "2023-09-19T16:03:28.707586Z",
-     "iopub.status.idle": "2023-09-19T16:03:40.881646Z",
-     "shell.execute_reply": "2023-09-19T16:03:40.880200Z"
+     "iopub.execute_input": "2023-09-21T15:19:48.427311Z",
+     "iopub.status.busy": "2023-09-21T15:19:48.427053Z",
+     "iopub.status.idle": "2023-09-21T15:20:00.190060Z",
+     "shell.execute_reply": "2023-09-21T15:20:00.189377Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x800 with 1 Axes>"
       ]
@@ -227,16 +227,16 @@
    "id": "40b5a90f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:40.886062Z",
-     "iopub.status.busy": "2023-09-19T16:03:40.885233Z",
-     "iopub.status.idle": "2023-09-19T16:03:49.195408Z",
-     "shell.execute_reply": "2023-09-19T16:03:49.194417Z"
+     "iopub.execute_input": "2023-09-21T15:20:00.195951Z",
+     "iopub.status.busy": "2023-09-21T15:20:00.195751Z",
+     "iopub.status.idle": "2023-09-21T15:20:08.181487Z",
+     "shell.execute_reply": "2023-09-21T15:20:08.180840Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x800 with 1 Axes>"
       ]
@@ -285,10 +285,10 @@
    "id": "9ba03fac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:03:49.199161Z",
-     "iopub.status.busy": "2023-09-19T16:03:49.198960Z",
-     "iopub.status.idle": "2023-09-19T16:04:05.606446Z",
-     "shell.execute_reply": "2023-09-19T16:04:05.605689Z"
+     "iopub.execute_input": "2023-09-21T15:20:08.187450Z",
+     "iopub.status.busy": "2023-09-21T15:20:08.187246Z",
+     "iopub.status.idle": "2023-09-21T15:20:24.439511Z",
+     "shell.execute_reply": "2023-09-21T15:20:24.437810Z"
     }
    },
    "outputs": [
@@ -305,7 +305,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6):  17%|█▋        | 4266/25596 [00:00<00:00, 33320.36 examples/s]"
+      "Filter (num_proc=6):  17%|█▋        | 4266/25596 [00:00<00:00, 32117.36 examples/s]"
      ]
     },
     {
@@ -313,7 +313,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6):  50%|█████     | 12798/25596 [00:00<00:00, 58152.98 examples/s]"
+      "Filter (num_proc=6):  50%|█████     | 12798/25596 [00:00<00:00, 59308.24 examples/s]"
      ]
     },
     {
@@ -321,7 +321,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter (num_proc=6):  83%|████████▎ | 21330/25596 [00:00<00:00, 68043.09 examples/s]"
+      "Filter (num_proc=6):  83%|████████▎ | 21330/25596 [00:00<00:00, 68146.59 examples/s]"
      ]
     },
     {
@@ -329,7 +329,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
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      ]
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      ]
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-     ]
-    },
-    {
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      ]
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      ]
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      ]
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-     ]
-    },
-    {
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-     "output_type": "stream",
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-      "\r",
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      ]
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      ]
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      ]
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      ]
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       "\r",
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      ]
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      "output_type": "stream",
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      ]
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@@ -540,7 +524,7 @@
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      ]
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      ]
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@@ -556,7 +540,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -564,7 +548,7 @@
      "output_type": "stream",
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       "\r",
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      ]
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@@ -587,7 +571,7 @@
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@@ -595,7 +579,7 @@
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      ]
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@@ -603,7 +587,7 @@
      "output_type": "stream",
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       "\r",
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      ]
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      "output_type": "stream",
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       "\r",
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      ]
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@@ -634,7 +618,7 @@
      "output_type": "stream",
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      ]
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@@ -642,7 +626,7 @@
      "output_type": "stream",
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       "\r",
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      ]
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     {
@@ -650,7 +634,7 @@
      "output_type": "stream",
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       "\r",
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      ]
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     {
@@ -658,7 +642,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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     {
@@ -670,7 +654,7 @@
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",
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",
       "text/plain": [
        "<Figure size 1100x800 with 1 Axes>"
       ]
@@ -734,17 +718,17 @@
    "id": "77e4b383",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:05.613022Z",
-     "iopub.status.busy": "2023-09-19T16:04:05.612784Z",
-     "iopub.status.idle": "2023-09-19T16:04:07.843552Z",
-     "shell.execute_reply": "2023-09-19T16:04:07.842929Z"
+     "iopub.execute_input": "2023-09-21T15:20:24.445864Z",
+     "iopub.status.busy": "2023-09-21T15:20:24.445335Z",
+     "iopub.status.idle": "2023-09-21T15:20:26.691388Z",
+     "shell.execute_reply": "2023-09-21T15:20:26.690729Z"
     },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x1000 with 1 Axes>"
       ]
diff --git a/api/tutorials/omop/query_api.html b/api/tutorials/omop/query_api.html
index e2ff37e1e..46e741409 100644
--- a/api/tutorials/omop/query_api.html
+++ b/api/tutorials/omop/query_api.html
@@ -492,9 +492,9 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
-2023-09-19 12:04:13,984 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:20:32,084 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -568,8 +568,8 @@ <h2>Example 1. Get all patient visits in or after 2010.<a class="headerlink" hre
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:15,230 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:15,232 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.065107 s
+2023-09-21 11:20:32,762 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:20:32,764 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.073394 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -626,8 +626,8 @@ <h2>Example 2. Get measurements for all visits in or after 2020.<a class="header
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:15,333 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:15,335 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.058644 s
+2023-09-21 11:20:32,876 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:20:32,877 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.060985 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -673,7 +673,7 @@ <h2>Imports and instantiate <code class="docutils literal notranslate"><span cla
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:19,376 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:20:39,668 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -713,8 +713,8 @@ <h2>Example 1. Get all patient visits that ended in a mortality outcome in or af
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:25,423 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:25,424 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.941731 s
+2023-09-21 11:20:47,657 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:20:47,658 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.982651 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -770,8 +770,8 @@ <h2>Example 2. Get all measurements for female patient visits with <code class="
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:42,055 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:42,056 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 16.424603 s
+2023-09-21 11:21:03,713 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:03,714 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 15.987421 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/api/tutorials/omop/query_api.ipynb b/api/tutorials/omop/query_api.ipynb
index 7f58d4a07..284848849 100644
--- a/api/tutorials/omop/query_api.ipynb
+++ b/api/tutorials/omop/query_api.ipynb
@@ -45,10 +45,10 @@
    "id": "53009e6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:11.330025Z",
-     "iopub.status.busy": "2023-09-19T16:04:11.328536Z",
-     "iopub.status.idle": "2023-09-19T16:04:15.135326Z",
-     "shell.execute_reply": "2023-09-19T16:04:15.134171Z"
+     "iopub.execute_input": "2023-09-21T15:20:29.968455Z",
+     "iopub.status.busy": "2023-09-21T15:20:29.967832Z",
+     "iopub.status.idle": "2023-09-21T15:20:32.670741Z",
+     "shell.execute_reply": "2023-09-21T15:20:32.669483Z"
     }
    },
    "outputs": [
@@ -56,7 +56,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -64,7 +64,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:13,984 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:20:32,084 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
@@ -158,10 +158,10 @@
    "id": "3a3d9cb9-fe40-45b8-ba2f-8de52a3b7f4f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:15.139922Z",
-     "iopub.status.busy": "2023-09-19T16:04:15.139176Z",
-     "iopub.status.idle": "2023-09-19T16:04:15.242180Z",
-     "shell.execute_reply": "2023-09-19T16:04:15.241445Z"
+     "iopub.execute_input": "2023-09-21T15:20:32.675784Z",
+     "iopub.status.busy": "2023-09-21T15:20:32.675350Z",
+     "iopub.status.idle": "2023-09-21T15:20:32.780285Z",
+     "shell.execute_reply": "2023-09-21T15:20:32.778884Z"
     }
    },
    "outputs": [
@@ -169,14 +169,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:15,230 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:20:32,762 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:15,232 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.065107 s\n"
+      "2023-09-21 11:20:32,764 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.073394 s\n"
      ]
     },
     {
@@ -233,10 +233,10 @@
    "id": "030e2491-a7cc-42f3-a1ca-618212b3524c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:15.245307Z",
-     "iopub.status.busy": "2023-09-19T16:04:15.244991Z",
-     "iopub.status.idle": "2023-09-19T16:04:15.341315Z",
-     "shell.execute_reply": "2023-09-19T16:04:15.340021Z"
+     "iopub.execute_input": "2023-09-21T15:20:32.787408Z",
+     "iopub.status.busy": "2023-09-21T15:20:32.787048Z",
+     "iopub.status.idle": "2023-09-21T15:20:32.883542Z",
+     "shell.execute_reply": "2023-09-21T15:20:32.882113Z"
     }
    },
    "outputs": [
@@ -244,14 +244,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:15,333 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:20:32,876 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:15,335 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.058644 s\n"
+      "2023-09-21 11:20:32,877 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.060985 s\n"
      ]
     },
     {
@@ -309,10 +309,10 @@
    "id": "0622b3df-2864-4f32-bd98-806019f59c50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:15.346103Z",
-     "iopub.status.busy": "2023-09-19T16:04:15.344976Z",
-     "iopub.status.idle": "2023-09-19T16:04:24.443344Z",
-     "shell.execute_reply": "2023-09-19T16:04:24.441688Z"
+     "iopub.execute_input": "2023-09-21T15:20:32.887960Z",
+     "iopub.status.busy": "2023-09-21T15:20:32.887658Z",
+     "iopub.status.idle": "2023-09-21T15:20:46.636115Z",
+     "shell.execute_reply": "2023-09-21T15:20:46.634435Z"
     },
     "tags": []
    },
@@ -321,7 +321,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:19,376 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:20:39,668 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
@@ -363,10 +363,10 @@
    "id": "40ff2e83-75e4-4119-aa33-26f95e63ddaa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:24.452007Z",
-     "iopub.status.busy": "2023-09-19T16:04:24.451387Z",
-     "iopub.status.idle": "2023-09-19T16:04:25.428661Z",
-     "shell.execute_reply": "2023-09-19T16:04:25.427904Z"
+     "iopub.execute_input": "2023-09-21T15:20:46.643291Z",
+     "iopub.status.busy": "2023-09-21T15:20:46.642703Z",
+     "iopub.status.idle": "2023-09-21T15:20:47.661643Z",
+     "shell.execute_reply": "2023-09-21T15:20:47.661030Z"
     },
     "tags": []
    },
@@ -375,14 +375,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:25,423 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:20:47,657 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:25,424 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.941731 s\n"
+      "2023-09-21 11:20:47,658 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.982651 s\n"
      ]
     },
     {
@@ -425,10 +425,10 @@
    "id": "46fd771c-5da7-4bce-aec7-08a5210a069b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:25.432310Z",
-     "iopub.status.busy": "2023-09-19T16:04:25.432114Z",
-     "iopub.status.idle": "2023-09-19T16:04:42.060086Z",
-     "shell.execute_reply": "2023-09-19T16:04:42.059155Z"
+     "iopub.execute_input": "2023-09-21T15:20:47.668887Z",
+     "iopub.status.busy": "2023-09-21T15:20:47.668641Z",
+     "iopub.status.idle": "2023-09-21T15:21:03.719854Z",
+     "shell.execute_reply": "2023-09-21T15:21:03.718648Z"
     },
     "tags": []
    },
@@ -437,14 +437,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:42,055 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:03,713 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:42,056 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 16.424603 s\n"
+      "2023-09-21 11:21:03,714 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 15.987421 s\n"
      ]
     },
     {
@@ -494,10 +494,10 @@
    "id": "d20a2581-f613-4ab8-9feb-3e84b8835db1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:04:42.067249Z",
-     "iopub.status.busy": "2023-09-19T16:04:42.067015Z",
-     "iopub.status.idle": "2023-09-19T16:04:42.075048Z",
-     "shell.execute_reply": "2023-09-19T16:04:42.073988Z"
+     "iopub.execute_input": "2023-09-21T15:21:03.726216Z",
+     "iopub.status.busy": "2023-09-21T15:21:03.725634Z",
+     "iopub.status.idle": "2023-09-21T15:21:03.736676Z",
+     "shell.execute_reply": "2023-09-21T15:21:03.735134Z"
     },
     "tags": []
    },
diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html
index 7eaab421f..13de9722c 100644
--- a/api/tutorials/synthea/los_prediction.html
+++ b/api/tutorials/synthea/los_prediction.html
@@ -492,7 +492,7 @@ <h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link t
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 </pre></div></div>
 </div>
@@ -676,17 +676,17 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:04:50,811 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
-2023-09-19 12:04:53,739 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:53,740 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 2.469421 s
-2023-09-19 12:04:55,587 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:55,588 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 1.845824 s
-2023-09-19 12:04:56,876 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:56,876 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.120768 s
-2023-09-19 12:04:57,033 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:57,034 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.151132 s
-2023-09-19 12:04:57,111 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
-2023-09-19 12:04:57,112 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.077010 s
+2023-09-21 11:21:11,813 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Database setup, ready to run queries!
+2023-09-21 11:21:16,638 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:16,638 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 3.832714 s
+2023-09-21 11:21:18,455 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:18,456 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 1.816797 s
+2023-09-21 11:21:20,032 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:20,034 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.385914 s
+2023-09-21 11:21:20,526 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:20,528 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.488658 s
+2023-09-21 11:21:20,627 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.query.orm - Query returned successfully!
+2023-09-21 11:21:20,628 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.utils.profile - Finished executing function run_query in 0.098377 s
 </pre></div></div>
 </div>
 </section>
@@ -773,9 +773,9 @@ <h3>Drop NaNs based on the <code class="docutils literal notranslate"><span clas
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
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@@ -1152,7 +1152,7 @@ <h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-ty
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-{&#39;obs_Urea nitrogen [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;obs_MCHC [Mass/volume] by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Erythrocyte distribution width [Ratio] by Automated count&#39;: &#39;numeric&#39;, &#39;obs_MCH [Entitic mass] by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Systolic Blood Pressure&#39;: &#39;numeric&#39;, &#39;obs_Glomerular filtration rate/1.73 sq M.predicted&#39;: &#39;numeric&#39;, &#39;obs_MCV [Entitic volume] by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;n_obs&#39;: &#39;numeric&#39;, &#39;obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Diastolic Blood Pressure&#39;: &#39;numeric&#39;, &#39;obs_Erythrocytes [#/volume] in Blood by Automated count&#39;: &#39;numeric&#39;, &#39;outcome&#39;: &#39;binary&#39;, &#39;obs_Body Weight&#39;: &#39;numeric&#39;, &#39;obs_Hematocrit [Volume Fraction] of Blood by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Bilirubin.total [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Glucose [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method&#39;: &#39;numeric&#39;, &#39;obs_Albumin [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Calcium [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Hemoglobin [Mass/volume] in Blood&#39;: &#39;numeric&#39;, &#39;obs_Potassium [Moles/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Sodium [Moles/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;age&#39;: &#39;numeric&#39;, &#39;obs_Leukocytes [#/volume] in Blood by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Platelets [#/volume] in Blood by Automated count&#39;: &#39;numeric&#39;, &#39;obs_Protein [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Chloride [Moles/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Oxygen saturation in Arterial blood&#39;: &#39;numeric&#39;, &#39;obs_Ferritin [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;n_meds&#39;: &#39;numeric&#39;, &#39;obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;obs_Creatinine [Mass/volume] in Serum or Plasma&#39;: &#39;numeric&#39;, &#39;n_procedures&#39;: &#39;numeric&#39;}
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@@ -1270,7 +1270,7 @@ <h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-F
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-Casting the dataset: 100%|██████████| 1126/1126 [00:00&lt;00:00, 148571.36 examples/s]
+Casting the dataset: 100%|██████████| 1126/1126 [00:00&lt;00:00, 111602.30 examples/s]
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@@ -1351,12 +1351,12 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2023-09-19 12:05:04,509 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 0
-2023-09-19 12:05:04,510 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 250
-2023-09-19 12:05:04,510 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
-2023-09-19 12:05:04,510 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.01
-2023-09-19 12:05:04,511 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 10
-2023-09-19 12:05:04,511 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2023-09-21 11:21:26,810 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 1
+2023-09-21 11:21:26,811 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2023-09-21 11:21:26,811 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
+2023-09-21 11:21:26,812 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.01
+2023-09-21 11:21:26,813 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 2
+2023-09-21 11:21:26,814 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1365,26 +1365,26 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
 <div class="output_area rendered_html docutils container">
 <style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=10, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.01, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=250, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=10, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.01, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=250, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre></div></div></div></div></div></div>
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.8, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.01, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.8, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.01, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div></div></div></div></div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
@@ -1400,7 +1400,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 1, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 10, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.01, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 250, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 0, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
+{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 0.8, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 2, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.01, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 500, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 1, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
 </pre></div></div>
 </div>
 <p><strong>Log the model parameters to the report.</strong></p>
@@ -1437,7 +1437,7 @@ <h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this headi
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Map: 100%|██████████| 226/226 [00:00&lt;00:00, 4185.43 examples/s]
+Map: 100%|██████████| 226/226 [00:00&lt;00:00, 4109.87 examples/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -1578,20 +1578,20 @@ <h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this headi
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Map: 100%|██████████| 226/226 [00:00&lt;00:00, 4084.18 examples/s]
-Flattening the indices: 100%|██████████| 226/226 [00:00&lt;00:00, 3919.89 examples/s]
-Flattening the indices: 100%|██████████| 226/226 [00:00&lt;00:00, 71239.49 examples/s]
-Filter -&gt; age:[20 - 50): 100%|██████████| 226/226 [00:00&lt;00:00, 8646.71 examples/s]
-Filter -&gt; age:[50 - 80): 100%|██████████| 226/226 [00:00&lt;00:00, 9190.01 examples/s]
-Filter -&gt; gender:M: 100%|██████████| 226/226 [00:00&lt;00:00, 9311.25 examples/s]
-Filter -&gt; gender:F: 100%|██████████| 226/226 [00:00&lt;00:00, 9276.07 examples/s]
-Filter -&gt; overall: 100%|██████████| 226/226 [00:00&lt;00:00, 5004.05 examples/s]
-Filter -&gt; gender:M&amp;age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8760.91 examples/s]
-Filter -&gt; gender:M&amp;age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8913.56 examples/s]
-Filter -&gt; gender:M&amp;age:(40.0 - inf]: 100%|██████████| 226/226 [00:00&lt;00:00, 8720.29 examples/s]
-Filter -&gt; gender:F&amp;age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8836.29 examples/s]
-Filter -&gt; gender:F&amp;age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 9122.18 examples/s]
-Filter -&gt; gender:F&amp;age:(40.0 - inf]: 100%|██████████| 226/226 [00:00&lt;00:00, 9052.31 examples/s]
+Map: 100%|██████████| 226/226 [00:00&lt;00:00, 3868.23 examples/s]
+Flattening the indices: 100%|██████████| 226/226 [00:00&lt;00:00, 3802.94 examples/s]
+Flattening the indices: 100%|██████████| 226/226 [00:00&lt;00:00, 53092.46 examples/s]
+Filter -&gt; age:[20 - 50): 100%|██████████| 226/226 [00:00&lt;00:00, 7812.88 examples/s]
+Filter -&gt; age:[50 - 80): 100%|██████████| 226/226 [00:00&lt;00:00, 8117.01 examples/s]
+Filter -&gt; gender:M: 100%|██████████| 226/226 [00:00&lt;00:00, 7811.14 examples/s]
+Filter -&gt; gender:F: 100%|██████████| 226/226 [00:00&lt;00:00, 8648.84 examples/s]
+Filter -&gt; overall: 100%|██████████| 226/226 [00:00&lt;00:00, 9174.53 examples/s]
+Filter -&gt; gender:M&amp;age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8333.96 examples/s]
+Filter -&gt; gender:M&amp;age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8439.62 examples/s]
+Filter -&gt; gender:M&amp;age:(40.0 - inf]: 100%|██████████| 226/226 [00:00&lt;00:00, 7456.66 examples/s]
+Filter -&gt; gender:F&amp;age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8146.87 examples/s]
+Filter -&gt; gender:F&amp;age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00&lt;00:00, 8015.43 examples/s]
+Filter -&gt; gender:F&amp;age:(40.0 - inf]: 100%|██████████| 226/226 [00:00&lt;00:00, 7681.19 examples/s]
 </pre></div></div>
 </div>
 <p><strong>Log the performance metrics to the report.</strong></p>
@@ -1615,7 +1615,7 @@ <h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this headi
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;age:[20 - 50)/BinaryAccuracy&#39;: 0.847457627118644, &#39;age:[20 - 50)/BinaryPrecision&#39;: 0.9807692307692307, &#39;age:[20 - 50)/BinaryRecall&#39;: 0.75, &#39;age:[20 - 50)/BinaryF1Score&#39;: 0.85, &#39;age:[20 - 50)/BinaryAUROC&#39;: 0.9257352941176471, &#39;age:[50 - 80)/BinaryAccuracy&#39;: 0.86, &#39;age:[50 - 80)/BinaryPrecision&#39;: 0.95, &#39;age:[50 - 80)/BinaryRecall&#39;: 0.76, &#39;age:[50 - 80)/BinaryF1Score&#39;: 0.8444444444444444, &#39;age:[50 - 80)/BinaryAUROC&#39;: 0.9336, &#39;gender:M/BinaryAccuracy&#39;: 0.8790322580645161, &#39;gender:M/BinaryPrecision&#39;: 1.0, &#39;gender:M/BinaryRecall&#39;: 0.8170731707317073, &#39;gender:M/BinaryF1Score&#39;: 0.8993288590604027, &#39;gender:M/BinaryAUROC&#39;: 0.954849012775842, &#39;gender:F/BinaryAccuracy&#39;: 0.8627450980392157, &#39;gender:F/BinaryPrecision&#39;: 0.9622641509433962, &#39;gender:F/BinaryRecall&#39;: 0.8095238095238095, &#39;gender:F/BinaryF1Score&#39;: 0.8793103448275862, &#39;gender:F/BinaryAUROC&#39;: 0.936100936100936, &#39;overall/BinaryAccuracy&#39;: 0.8716814159292036, &#39;overall/BinaryPrecision&#39;: 0.9833333333333333, &#39;overall/BinaryRecall&#39;: 0.8137931034482758, &#39;overall/BinaryF1Score&#39;: 0.8905660377358491, &#39;overall/BinaryAUROC&#39;: 0.946743295019157}
+{&#39;age:[20 - 50)/BinaryAccuracy&#39;: 0.8859649122807017, &#39;age:[20 - 50)/BinaryPrecision&#39;: 0.8873239436619719, &#39;age:[20 - 50)/BinaryRecall&#39;: 0.9264705882352942, &#39;age:[20 - 50)/BinaryF1Score&#39;: 0.9064748201438849, &#39;age:[20 - 50)/BinaryAUROC&#39;: 0.9638746803069054, &#39;age:[50 - 80)/BinaryAccuracy&#39;: 0.8909090909090909, &#39;age:[50 - 80)/BinaryPrecision&#39;: 0.8888888888888888, &#39;age:[50 - 80)/BinaryRecall&#39;: 0.8888888888888888, &#39;age:[50 - 80)/BinaryF1Score&#39;: 0.8888888888888888, &#39;age:[50 - 80)/BinaryAUROC&#39;: 0.9735449735449735, &#39;gender:M/BinaryAccuracy&#39;: 0.9126984126984127, &#39;gender:M/BinaryPrecision&#39;: 0.9285714285714286, &#39;gender:M/BinaryRecall&#39;: 0.9397590361445783, &#39;gender:M/BinaryF1Score&#39;: 0.9341317365269461, &#39;gender:M/BinaryAUROC&#39;: 0.9775847576351919, &#39;gender:F/BinaryAccuracy&#39;: 0.9, &#39;gender:F/BinaryPrecision&#39;: 0.9193548387096774, &#39;gender:F/BinaryRecall&#39;: 0.9193548387096774, &#39;gender:F/BinaryF1Score&#39;: 0.9193548387096774, &#39;gender:F/BinaryAUROC&#39;: 0.9685908319185059, &#39;overall/BinaryAccuracy&#39;: 0.9070796460176991, &#39;overall/BinaryPrecision&#39;: 0.9246575342465754, &#39;overall/BinaryRecall&#39;: 0.9310344827586207, &#39;overall/BinaryF1Score&#39;: 0.9278350515463918, &#39;overall/BinaryAUROC&#39;: 0.9728395061728395}
 </pre></div></div>
 </div>
 <div class="nbinput nblast docutils container">
@@ -1691,9 +1691,9 @@ <h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this headi
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
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@@ -44,7 +44,7 @@
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      "output_type": "stream",
      "text": [
-      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.9/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
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@@ -107,10 +107,10 @@
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    "metadata": {
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-     "iopub.status.busy": "2023-09-19T16:04:49.752544Z",
-     "iopub.status.idle": "2023-09-19T16:04:49.758513Z",
-     "shell.execute_reply": "2023-09-19T16:04:49.757319Z"
+     "iopub.execute_input": "2023-09-21T15:21:10.765130Z",
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@@ -133,10 +133,10 @@
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-     "iopub.status.busy": "2023-09-19T16:04:49.761571Z",
-     "iopub.status.idle": "2023-09-19T16:04:49.769681Z",
-     "shell.execute_reply": "2023-09-19T16:04:49.768335Z"
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@@ -167,10 +167,10 @@
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    "metadata": {
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-     "iopub.execute_input": "2023-09-19T16:04:49.774491Z",
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-     "iopub.status.idle": "2023-09-19T16:04:57.131097Z",
-     "shell.execute_reply": "2023-09-19T16:04:57.130419Z"
+     "iopub.execute_input": "2023-09-21T15:21:10.782647Z",
+     "iopub.status.busy": "2023-09-21T15:21:10.782129Z",
+     "iopub.status.idle": "2023-09-21T15:21:20.655558Z",
+     "shell.execute_reply": "2023-09-21T15:21:20.654789Z"
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@@ -179,77 +179,77 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:50,811 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
+      "2023-09-21 11:21:11,813 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Database setup, ready to run queries!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:53,739 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:16,638 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:53,740 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 2.469421 s\n"
+      "2023-09-21 11:21:16,638 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 3.832714 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:55,587 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:18,455 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:55,588 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.845824 s\n"
+      "2023-09-21 11:21:18,456 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 1.816797 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:56,876 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:20,032 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:56,876 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.120768 s\n"
+      "2023-09-21 11:21:20,034 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.385914 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:57,033 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:20,526 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:57,034 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.151132 s\n"
+      "2023-09-21 11:21:20,528 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.488658 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:57,111 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
+      "2023-09-21 11:21:20,627 \u001b[1;37mINFO\u001b[0m cyclops.query.orm - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:04:57,112 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.077010 s\n"
+      "2023-09-21 11:21:20,628 \u001b[1;37mINFO\u001b[0m cyclops.utils.profile - Finished executing function run_query in 0.098377 s\n"
      ]
     }
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@@ -405,10 +405,10 @@
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@@ -448,10 +448,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.254854Z"
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@@ -8899,10 +8899,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.638975Z"
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@@ -8931,10 +8931,10 @@
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-     "iopub.status.idle": "2023-09-19T16:04:57.688787Z",
-     "shell.execute_reply": "2023-09-19T16:04:57.687915Z"
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@@ -8943,7 +8943,7 @@
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-      "{'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'gender': 'binary', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'n_obs': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'outcome': 'binary', 'obs_Body Weight': 'numeric', 'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'age': 'numeric', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'n_meds': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'n_procedures': 'numeric'}\n"
+      "{'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'n_obs': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric', 'age': 'numeric', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'gender': 'binary', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'n_procedures': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'n_meds': 'numeric', 'obs_Body Weight': 'numeric', 'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'outcome': 'binary', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric'}\n"
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@@ -9011,10 +9011,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.695455Z"
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+     "iopub.status.busy": "2023-09-21T15:21:21.300200Z",
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@@ -9035,10 +9035,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.735197Z"
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@@ -9065,10 +9065,10 @@
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@@ -9096,10 +9096,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.754293Z"
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@@ -9130,10 +9130,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.777425Z"
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@@ -9161,10 +9161,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.824352Z"
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@@ -9182,7 +9182,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Casting the dataset: 100%|██████████| 1126/1126 [00:00<00:00, 148571.36 examples/s]"
+      "Casting the dataset: 100%|██████████| 1126/1126 [00:00<00:00, 111602.30 examples/s]"
      ]
     },
     {
@@ -9218,10 +9218,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:57.970245Z"
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@@ -9247,10 +9247,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:58.028966Z"
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@@ -9269,10 +9269,10 @@
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-     "shell.execute_reply": "2023-09-19T16:04:58.037567Z"
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@@ -9310,10 +9310,10 @@
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@@ -9322,81 +9322,81 @@
      "name": "stderr",
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      "text": [
-      "2023-09-19 12:05:04,509 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 0\n"
+      "2023-09-21 11:21:26,810 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:05:04,510 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 250\n"
+      "2023-09-21 11:21:26,811 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:05:04,510 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2023-09-21 11:21:26,811 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:05:04,510 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.01\n"
+      "2023-09-21 11:21:26,812 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.01\n"
      ]
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:05:04,511 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 10\n"
+      "2023-09-21 11:21:26,813 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 2\n"
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-09-19 12:05:04,511 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 1\n"
+      "2023-09-21 11:21:26,814 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8\n"
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        "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=10, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=10, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre></div></div></div></div></div>"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric='logloss', feature_types=None, gamma=10, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric='logloss',\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)"
       ]
      },
      "execution_count": 28,
@@ -9428,10 +9428,10 @@
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-     "shell.execute_reply": "2023-09-19T16:05:04.523482Z"
+     "iopub.execute_input": "2023-09-21T15:21:26.826272Z",
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@@ -9440,7 +9440,7 @@
      "name": "stdout",
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      "text": [
-      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 10, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 250, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
+      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
      ]
     }
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@@ -9465,10 +9465,10 @@
    "id": "4cfdfe29-c813-45cd-9ae7-2959daa0cd8f",
    "metadata": {
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-     "iopub.status.busy": "2023-09-19T16:05:04.528316Z",
-     "iopub.status.idle": "2023-09-19T16:05:04.533273Z",
-     "shell.execute_reply": "2023-09-19T16:05:04.532679Z"
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@@ -9492,10 +9492,10 @@
    "id": "c3fee35c-902e-4c54-89b7-c42c1e935e1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-19T16:05:04.535971Z",
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-     "shell.execute_reply": "2023-09-19T16:05:04.618277Z"
+     "iopub.execute_input": "2023-09-21T15:21:26.847731Z",
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     },
     "tags": []
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@@ -9513,7 +9513,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|██████████| 226/226 [00:00<00:00, 4185.43 examples/s]"
+      "Map: 100%|██████████| 226/226 [00:00<00:00, 4109.87 examples/s]"
      ]
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     {
@@ -9560,10 +9560,10 @@
    "id": "d3d5b101-17a8-4610-ae17-e04cf962d2d1",
    "metadata": {
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-     "shell.execute_reply": "2023-09-19T16:05:04.630541Z"
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@@ -9596,10 +9596,10 @@
    "id": "06d72639-db3b-4e91-be9d-3bf9dbb0f0e8",
    "metadata": {
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-     "shell.execute_reply": "2023-09-19T16:05:04.642034Z"
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@@ -9642,10 +9642,10 @@
    "id": "24877057-f737-43bf-a61d-3dd4e45e0381",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-09-19T16:05:04.651559Z"
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@@ -9688,10 +9688,10 @@
    "id": "945fdb5c-1b55-42f8-9644-e2bf6efd7011",
    "metadata": {
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@@ -9722,10 +9722,10 @@
    "id": "3797fe8c-886e-426f-8ea9-ad5afad3b65b",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-09-19T16:05:05.349510Z"
+     "iopub.execute_input": "2023-09-21T15:21:26.976691Z",
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     },
     "tags": []
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@@ -9743,7 +9743,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|██████████| 226/226 [00:00<00:00, 4084.18 examples/s]"
+      "Map: 100%|██████████| 226/226 [00:00<00:00, 3868.23 examples/s]"
      ]
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     {
@@ -9766,7 +9766,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Flattening the indices: 100%|██████████| 226/226 [00:00<00:00, 3919.89 examples/s]"
+      "Flattening the indices: 100%|██████████| 226/226 [00:00<00:00, 3802.94 examples/s]"
      ]
     },
     {
@@ -9789,7 +9789,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Flattening the indices: 100%|██████████| 226/226 [00:00<00:00, 71239.49 examples/s]"
+      "Flattening the indices: 100%|██████████| 226/226 [00:00<00:00, 53092.46 examples/s]"
      ]
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     {
@@ -9812,7 +9812,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> age:[20 - 50): 100%|██████████| 226/226 [00:00<00:00, 8646.71 examples/s]"
+      "Filter -> age:[20 - 50): 100%|██████████| 226/226 [00:00<00:00, 7812.88 examples/s]"
      ]
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     {
@@ -9835,7 +9835,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> age:[50 - 80): 100%|██████████| 226/226 [00:00<00:00, 9190.01 examples/s]"
+      "Filter -> age:[50 - 80): 100%|██████████| 226/226 [00:00<00:00, 8117.01 examples/s]"
      ]
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     {
@@ -9858,7 +9858,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:M: 100%|██████████| 226/226 [00:00<00:00, 9311.25 examples/s]"
+      "Filter -> gender:M: 100%|██████████| 226/226 [00:00<00:00, 7811.14 examples/s]"
      ]
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     {
@@ -9881,7 +9881,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:F: 100%|██████████| 226/226 [00:00<00:00, 9276.07 examples/s]"
+      "Filter -> gender:F: 100%|██████████| 226/226 [00:00<00:00, 8648.84 examples/s]"
      ]
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     {
@@ -9904,7 +9904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall: 100%|██████████| 226/226 [00:00<00:00, 5004.05 examples/s]"
+      "Filter -> overall: 100%|██████████| 226/226 [00:00<00:00, 9174.53 examples/s]"
      ]
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     {
@@ -9927,7 +9927,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:M&age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00<00:00, 8760.91 examples/s]"
+      "Filter -> gender:M&age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00<00:00, 8333.96 examples/s]"
      ]
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     {
@@ -9950,7 +9950,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:M&age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00<00:00, 8913.56 examples/s]"
+      "Filter -> gender:M&age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00<00:00, 8439.62 examples/s]"
      ]
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     {
@@ -9973,7 +9973,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:M&age:(40.0 - inf]: 100%|██████████| 226/226 [00:00<00:00, 8720.29 examples/s]"
+      "Filter -> gender:M&age:(40.0 - inf]: 100%|██████████| 226/226 [00:00<00:00, 7456.66 examples/s]"
      ]
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     {
@@ -9996,7 +9996,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:F&age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00<00:00, 8836.29 examples/s]"
+      "Filter -> gender:F&age:(-inf - 20.0]: 100%|██████████| 226/226 [00:00<00:00, 8146.87 examples/s]"
      ]
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     {
@@ -10019,7 +10019,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:F&age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00<00:00, 9122.18 examples/s]"
+      "Filter -> gender:F&age:(20.0 - 40.0]: 100%|██████████| 226/226 [00:00<00:00, 8015.43 examples/s]"
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     {
@@ -10042,7 +10042,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> gender:F&age:(40.0 - inf]: 100%|██████████| 226/226 [00:00<00:00, 9052.31 examples/s]"
+      "Filter -> gender:F&age:(40.0 - inf]: 100%|██████████| 226/226 [00:00<00:00, 7681.19 examples/s]"
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@@ -10087,10 +10087,10 @@
    "id": "d322a86f-1f7c-42f6-8a97-8a18ea8622e2",
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-     "shell.execute_reply": "2023-09-19T16:05:05.357978Z"
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@@ -10099,7 +10099,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'age:[20 - 50)/BinaryAccuracy': 0.847457627118644, 'age:[20 - 50)/BinaryPrecision': 0.9807692307692307, 'age:[20 - 50)/BinaryRecall': 0.75, 'age:[20 - 50)/BinaryF1Score': 0.85, 'age:[20 - 50)/BinaryAUROC': 0.9257352941176471, 'age:[50 - 80)/BinaryAccuracy': 0.86, 'age:[50 - 80)/BinaryPrecision': 0.95, 'age:[50 - 80)/BinaryRecall': 0.76, 'age:[50 - 80)/BinaryF1Score': 0.8444444444444444, 'age:[50 - 80)/BinaryAUROC': 0.9336, 'gender:M/BinaryAccuracy': 0.8790322580645161, 'gender:M/BinaryPrecision': 1.0, 'gender:M/BinaryRecall': 0.8170731707317073, 'gender:M/BinaryF1Score': 0.8993288590604027, 'gender:M/BinaryAUROC': 0.954849012775842, 'gender:F/BinaryAccuracy': 0.8627450980392157, 'gender:F/BinaryPrecision': 0.9622641509433962, 'gender:F/BinaryRecall': 0.8095238095238095, 'gender:F/BinaryF1Score': 0.8793103448275862, 'gender:F/BinaryAUROC': 0.936100936100936, 'overall/BinaryAccuracy': 0.8716814159292036, 'overall/BinaryPrecision': 0.9833333333333333, 'overall/BinaryRecall': 0.8137931034482758, 'overall/BinaryF1Score': 0.8905660377358491, 'overall/BinaryAUROC': 0.946743295019157}\n"
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@@ -10118,10 +10118,10 @@
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@@ -11369,10 +11961,10 @@
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    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2023-09-19T16:05:05.481856Z",
-     "iopub.status.idle": "2023-09-19T16:05:05.486059Z",
-     "shell.execute_reply": "2023-09-19T16:05:05.485112Z"
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+     "iopub.status.idle": "2023-09-21T15:21:27.837500Z",
+     "shell.execute_reply": "2023-09-21T15:21:27.836856Z"
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@@ -11391,10 +11983,10 @@
    "id": "a264f7a3-db58-4cbf-a67b-63a9ffe46b98",
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     "execution": {
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-     "iopub.status.busy": "2023-09-19T16:05:05.490305Z",
-     "iopub.status.idle": "2023-09-19T16:05:05.511530Z",
-     "shell.execute_reply": "2023-09-19T16:05:05.510994Z"
+     "iopub.execute_input": "2023-09-21T15:21:27.841479Z",
+     "iopub.status.busy": "2023-09-21T15:21:27.841174Z",
+     "iopub.status.idle": "2023-09-21T15:21:27.865714Z",
+     "shell.execute_reply": "2023-09-21T15:21:27.865144Z"
     }
    },
    "outputs": [
@@ -11415,11 +12007,11 @@
           "BinaryAUROC"
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-          0.9833333333333333,
-          0.8137931034482758,
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+          0.9246575342465754,
+          0.9310344827586207,
+          0.9278350515463918,
+          0.9728395061728395
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        ],
@@ -12256,9 +12848,9 @@
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@@ -12307,10 +12899,10 @@
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-     "shell.execute_reply": "2023-09-19T16:05:05.521029Z"
+     "iopub.execute_input": "2023-09-21T15:21:27.872199Z",
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+     "shell.execute_reply": "2023-09-21T15:21:27.876387Z"
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@@ -12332,10 +12924,10 @@
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+     "shell.execute_reply": "2023-09-21T15:21:27.907431Z"
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@@ -12357,11 +12949,11 @@
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@@ -12375,11 +12967,11 @@
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 </div>
 
@@ -740,7 +740,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2023-09-19
+              <b>Date:</b> 2023-09-21
             
           
           <br>
@@ -952,6 +952,10 @@ <h2>Model Parameters</h2>
 
   
     
+    <div class="subcard">
+      <h3>Objective</h3>
+      binary:logistic
+    </div>
     
   
 
@@ -972,10 +976,6 @@ <h2>Model Parameters</h2>
 
   
     
-    <div class="subcard">
-      <h3>Min_child_weight</h3>
-      3
-    </div>
     
   
 
@@ -986,10 +986,6 @@ <h3>Min_child_weight</h3>
 
   
     
-    <div class="subcard">
-      <h3>Random_state</h3>
-      123
-    </div>
     
   
 
@@ -1000,38 +996,34 @@ <h3>Random_state</h3>
 
   
     
-    <div class="subcard">
-      <h3>Reg_lambda</h3>
-      0
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Enable_categorical</h3>
+      False
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Seed</h3>
-      123
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Eval_metric</h3>
-      logloss
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>N_estimators</h3>
+      500
+    </div>
     
   
 
@@ -1043,8 +1035,8 @@ <h3>Eval_metric</h3>
   
     
     <div class="subcard">
-      <h3>Colsample_bytree</h3>
-      1
+      <h3>Learning_rate</h3>
+      0.01
     </div>
     
   
@@ -1066,6 +1058,10 @@ <h3>Colsample_bytree</h3>
 
   
     
+    <div class="subcard">
+      <h3>Max_depth</h3>
+      5
+    </div>
     
   
 
@@ -1077,14 +1073,18 @@ <h3>Colsample_bytree</h3>
   
     
     <div class="subcard">
-      <h3>Objective</h3>
-      binary:logistic
+      <h3>Min_child_weight</h3>
+      3
     </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Colsample_bytree</h3>
+      0.8
+    </div>
     
   
 
@@ -1100,24 +1100,24 @@ <h3>Objective</h3>
 
   
     
+    <div class="subcard">
+      <h3>Seed</h3>
+      123
+    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Learning_rate</h3>
-      0.01
+      <h3>Reg_lambda</h3>
+      1
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>N_estimators</h3>
-      250
-    </div>
     
   
 
@@ -1133,15 +1133,15 @@ <h3>N_estimators</h3>
 
   
     
-    <div class="subcard">
-      <h3>Enable_categorical</h3>
-      False
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Random_state</h3>
+      123
+    </div>
     
   
 
@@ -1153,8 +1153,8 @@ <h3>Enable_categorical</h3>
   
     
     <div class="subcard">
-      <h3>Missing</h3>
-      nan
+      <h3>Gamma</h3>
+      2
     </div>
     
   
@@ -1167,8 +1167,8 @@ <h3>Missing</h3>
   
     
     <div class="subcard">
-      <h3>Gamma</h3>
-      10
+      <h3>Eval_metric</h3>
+      logloss
     </div>
     
   
@@ -1180,15 +1180,15 @@ <h3>Gamma</h3>
 
   
     
-    <div class="subcard">
-      <h3>Max_depth</h3>
-      5
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Missing</h3>
+      nan
+    </div>
     
   
 
@@ -1220,7 +1220,7 @@ <h3>Graphics</h3>
     
 <div class="img-item">
   
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 </div>
 
@@ -1496,7 +1496,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAccuracy</td>
           <td style="padding: 5px; text-align: center;">age:[20 - 50)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/age:[20 - 50)">0.85</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/age:[20 - 50)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1506,7 +1506,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryPrecision</td>
           <td style="padding: 5px; text-align: center;">age:[20 - 50)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/age:[20 - 50)">0.98</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/age:[20 - 50)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1516,7 +1516,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryRecall</td>
           <td style="padding: 5px; text-align: center;">age:[20 - 50)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryRecall/age:[20 - 50)">0.75</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryRecall/age:[20 - 50)">0.93</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1526,7 +1526,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryF1Score</td>
           <td style="padding: 5px; text-align: center;">age:[20 - 50)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/age:[20 - 50)">0.85</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/age:[20 - 50)">0.91</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1536,7 +1536,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAUROC</td>
           <td style="padding: 5px; text-align: center;">age:[20 - 50)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/age:[20 - 50)">0.93</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/age:[20 - 50)">0.96</td>
           
             <td style="padding: 5px; text-align: center;">0.8</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1546,7 +1546,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAccuracy</td>
           <td style="padding: 5px; text-align: center;">age:[50 - 80)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/age:[50 - 80)">0.86</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/age:[50 - 80)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1556,7 +1556,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryPrecision</td>
           <td style="padding: 5px; text-align: center;">age:[50 - 80)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/age:[50 - 80)">0.95</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/age:[50 - 80)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1566,7 +1566,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryRecall</td>
           <td style="padding: 5px; text-align: center;">age:[50 - 80)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryRecall/age:[50 - 80)">0.76</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryRecall/age:[50 - 80)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1576,7 +1576,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryF1Score</td>
           <td style="padding: 5px; text-align: center;">age:[50 - 80)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/age:[50 - 80)">0.84</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/age:[50 - 80)">0.89</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1586,7 +1586,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAUROC</td>
           <td style="padding: 5px; text-align: center;">age:[50 - 80)</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/age:[50 - 80)">0.93</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/age:[50 - 80)">0.97</td>
           
             <td style="padding: 5px; text-align: center;">0.8</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1596,7 +1596,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAccuracy</td>
           <td style="padding: 5px; text-align: center;">gender:M</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/gender:M">0.88</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/gender:M">0.91</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1606,7 +1606,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryPrecision</td>
           <td style="padding: 5px; text-align: center;">gender:M</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/gender:M">1.0</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/gender:M">0.93</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1616,7 +1616,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryRecall</td>
           <td style="padding: 5px; text-align: center;">gender:M</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryRecall/gender:M">0.82</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryRecall/gender:M">0.94</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1626,7 +1626,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryF1Score</td>
           <td style="padding: 5px; text-align: center;">gender:M</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/gender:M">0.9</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/gender:M">0.93</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1636,7 +1636,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAUROC</td>
           <td style="padding: 5px; text-align: center;">gender:M</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/gender:M">0.95</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/gender:M">0.98</td>
           
             <td style="padding: 5px; text-align: center;">0.8</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1646,7 +1646,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAccuracy</td>
           <td style="padding: 5px; text-align: center;">gender:F</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/gender:F">0.86</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/gender:F">0.9</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1656,7 +1656,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryPrecision</td>
           <td style="padding: 5px; text-align: center;">gender:F</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/gender:F">0.96</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/gender:F">0.92</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1666,7 +1666,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryRecall</td>
           <td style="padding: 5px; text-align: center;">gender:F</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryRecall/gender:F">0.81</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryRecall/gender:F">0.92</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1676,7 +1676,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryF1Score</td>
           <td style="padding: 5px; text-align: center;">gender:F</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/gender:F">0.88</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/gender:F">0.92</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1686,7 +1686,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAUROC</td>
           <td style="padding: 5px; text-align: center;">gender:F</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/gender:F">0.94</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/gender:F">0.97</td>
           
             <td style="padding: 5px; text-align: center;">0.8</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1696,7 +1696,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAccuracy</td>
           <td style="padding: 5px; text-align: center;">overall</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/overall">0.87</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAccuracy/overall">0.91</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1706,7 +1706,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryPrecision</td>
           <td style="padding: 5px; text-align: center;">overall</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/overall">0.98</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryPrecision/overall">0.92</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1716,7 +1716,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryRecall</td>
           <td style="padding: 5px; text-align: center;">overall</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryRecall/overall">0.81</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryRecall/overall">0.93</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1726,7 +1726,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryF1Score</td>
           <td style="padding: 5px; text-align: center;">overall</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/overall">0.89</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryF1Score/overall">0.93</td>
           
             <td style="padding: 5px; text-align: center;">0.6</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1736,7 +1736,7 @@ <h2>Quantitative Analysis</h2>
         <tr>
           <td style="padding: 5px; text-align: center;">BinaryAUROC</td>
           <td style="padding: 5px; text-align: center;">overall</td>
-          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/overall">0.95</td>
+          <td style="padding: 5px; text-align: center;" id="BinaryAUROC/overall">0.97</td>
           
             <td style="padding: 5px; text-align: center;">0.8</td>
             <td style="padding: 5px; text-align: center; color: green">Passed</td>
@@ -1766,7 +1766,7 @@ <h3>Graphics</h3>
     
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 <li class="toctree-l2"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1a.-Create-a-table-with-only-one-hospitalization-per-patient,-keeping-the-most-recent-encounter-for-each-patient.-Sort-the-dataset-by-patient_id_hashed-and-discharge_date_time,-and-then-keep-the-recent-record.">Example 1a. Create a table with only one hospitalization per patient, keeping the most recent encounter for each patient. Sort the dataset by <code class="docutils literal notranslate"><span class="pre">patient_id_hashed</span></code> and <code class="docutils literal notranslate"><span class="pre">discharge_date_time</span></code>, and then keep the recent record.</a></li>
 <li class="toctree-l2"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1b.-From-the-above-set-of-encounters,-take-a-subset-of-patients-who-were-discharged-between-April-1,-2015-and-March-31,-2016.">Example 1b. From the above set of encounters, take a subset of patients who were discharged between April 1, 2015 and March 31, 2016.</a></li>
 <li class="toctree-l2"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-1c.-From-the-above-set-of-encounters,-get-the-total-number-of-admissions-for-each-hospital.">Example 1c. From the above set of encounters, get the total number of admissions for each hospital.</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-SMH?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at SMH?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/gemini/query_api.html#Example-2a.-How-many-sodium-tests-were-placed-between-Apr-1,-2015-and-May-31,-2015-at-hospital-101?">Example 2a. How many sodium tests were placed between Apr 1, 2015 and May 31, 2015 at hospital 101?</a></li>
 </ul>
 </li>
 </ul>
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similarity index 100%
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diff --git a/assets/js/runtime~main.b8dcc3e6.js b/assets/js/runtime~main.c2b34449.js
similarity index 78%
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index 7f8729055..1cc8d10e8 100644
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 to deem that they are performant across patient sub-populations is crucial for adoption into clinical care.</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/cyclops/blog/tags/alpha">alpha</a></li></ul></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="row footer__links"><div class="col footer__col"><div class="footer__title">Docs</div><ul class="footer__items clean-list"><li class="footer__item"><a href="https://vectorinstitute.github.io/cyclops/api" target="_self" rel="noopener noreferrer" class="footer__link-item">API</a></li><li class="footer__item"><a href="https://vectorinstitute.github.io/cyclops/api/tutorials" target="_self" rel="noopener noreferrer" class="footer__link-item">Tutorial</a></li></ul></div><div class="col footer__col"><div class="footer__title">More</div><ul class="footer__items clean-list"><li class="footer__item"><a class="footer__link-item" href="/cyclops/blog">Blog</a></li><li class="footer__item"><a href="https://github.com/VectorInstitute/cyclops" target="_blank" rel="noopener noreferrer" class="footer__link-item">GitHub<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_nPIU"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"></path></svg></a></li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright">Copyright © 2023 CyclOps, built by AI Engineering @ Vector Institute</div></div></div></footer></div>
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clip-path="url(#b)" transform="matrix(.1336 0 0 -.13311 -9.472 2508.357)"><path d="m19942.3 5809.7-163.4-739 38.5-8.5 163.3 739-38.4 8.5M22642.5 5255.4l-163.4-739 38.4-8.5 163.4 739-38.4 8.5" style="fill:#fdc7ca;fill-opacity:1;fill-rule:nonzero;stroke:none"></path><path d="m25900 4454-163.4-739 38.4-8.5 163.4 739-38.4 8.5" style="fill:#9a9da8;fill-opacity:1;fill-rule:nonzero;stroke:none"></path></g><path d="m20649 5516.7 54.1 561.2c7.8 176.3-107.1 324.4-250.6 323.3-137.4-1-247.7-138.8-246.4-307.8l5.4-704.8c.8-109.1 75.9-195.3 164.4-188.8 141.5 10.5 256.5 143.8 273.1 316.9" style="fill:#ebc2ab;fill-opacity:1;fill-rule:nonzero;stroke:none" transform="matrix(.1336 0 0 -.13311 -9.472 2508.357)"></path></svg></div><div class="text--center padding-horiz--md"><h3>Rigorous Evaluation</h3><p>CyclOps APIs support rigorous evaluation across patient sub-populations</p></div></div><div class="col col--4"><div class="text--center"><svg viewBox="0 163.813 3300 1822.044" width="3300" height="1822.044" 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2357.39l75.7-177.67-1.9-12.76c-12.5-72.76-17.3-150.07-17.2-225.82.1-247.28 51.2-477.58 51.2-477.58 112.9-465.59 147.4-722.51 147.4-915.75 0-139.91-18-246.51-37.3-374.81-130.6-121.7-361.1-179.6-556.7-188.9" style="fill:#bddef5;fill-opacity:1;fill-rule:evenodd;stroke:none" transform="matrix(.13333 0 0 -.13333 0 2200)"></path><path d="M13487.9 8341.75s4.7-577.4 200.1-698.33c195.5-120.99 767.9 158.22 767.9 158.22s32.6 134.93-32.6 190.78l-553.8-49.45s24.5 593.72-79.1 688.52c-103.6 94.81-302.5-289.74-302.5-289.74" style="fill:#cee8f8;fill-opacity:1;fill-rule:evenodd;stroke:none" transform="matrix(.13333 0 0 -.13333 0 2200)"></path></svg></div><div class="text--center padding-horiz--md"><h3>Deployment and Operationalization</h3><p>By leveraging powerful open source tools, CyclOps provides a modular and extensible MLOps platform for healthcare</p></div></div><div class="col col--4"><div class="text--center"><svg width="955.907" height="529.853" viewBox="0 0 955.907 529.853" 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