From eb49aacec4c141954c091cac543f4986178b555e Mon Sep 17 00:00:00 2001
From: Christine Draper <christine@whylabs.ai>
Date: Fri, 3 May 2024 15:29:05 -0400
Subject: [PATCH 1/2] Fix column list

---
 .../customized_diagnoser.ipynb                | 112 +++++++-------
 examples/example_notebooks/diagnoser.ipynb    | 145 ++++++++++--------
 .../diagnoser/models/diagnosis_report.py      |   2 +-
 3 files changed, 136 insertions(+), 123 deletions(-)

diff --git a/examples/example_notebooks/customized_diagnoser.ipynb b/examples/example_notebooks/customized_diagnoser.ipynb
index 5f183a7..77791f6 100644
--- a/examples/example_notebooks/customized_diagnoser.ipynb
+++ b/examples/example_notebooks/customized_diagnoser.ipynb
@@ -17,14 +17,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 38,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:15.122705Z",
-     "start_time": "2024-05-03T02:03:15.119284Z"
+     "end_time": "2024-05-03T19:28:01.733321Z",
+     "start_time": "2024-05-03T19:28:01.731598Z"
     }
    },
    "outputs": [],
@@ -47,12 +47,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:31.740325Z",
-     "start_time": "2024-05-03T02:03:15.137102Z"
+     "end_time": "2024-05-03T19:28:09.883007Z",
+     "start_time": "2024-05-03T19:28:01.757481Z"
     }
    },
    "outputs": [],
@@ -84,12 +84,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:32.055442Z",
-     "start_time": "2024-05-03T02:03:31.743114Z"
+     "end_time": "2024-05-03T19:28:09.888906Z",
+     "start_time": "2024-05-03T19:28:09.884819Z"
     }
    },
    "outputs": [],
@@ -110,11 +110,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 41,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:32.690456Z",
-     "start_time": "2024-05-03T02:03:32.056136Z"
+     "end_time": "2024-05-03T19:28:10.298067Z",
+     "start_time": "2024-05-03T19:28:09.890844Z"
     }
    },
    "outputs": [
@@ -122,7 +122,7 @@
      "data": {
       "text/plain": "(TimeRange(start=datetime.datetime(2021, 5, 20, 0, 0, tzinfo=datetime.timezone.utc), end=datetime.datetime(2024, 5, 2, 21, 0, tzinfo=datetime.timezone.utc)),\n <Granularity.daily: 'daily'>,\n '2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z')"
      },
-     "execution_count": 4,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,12 +143,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.421897Z",
-     "start_time": "2024-05-03T02:03:32.692755Z"
+     "end_time": "2024-05-03T19:28:11.034284Z",
+     "start_time": "2024-05-03T19:28:10.300461Z"
     }
    },
    "outputs": [
@@ -157,7 +157,7 @@
       "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-x2hr9z   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-bx6m80   \n4        frequent-items-drift-monitor-mat0jo   \n5        frequent-items-drift-monitor-01rbfl   \n6        frequent-items-drift-monitor-0foigt   \n7        frequent-items-drift-monitor-3c0hc2   \n8        frequent-items-drift-monitor-9gmtix   \n9               elated-palegreen-jaguar-6432   \n10       inferred-data-type-fec5a735-monitor   \n11             unique-ratio-b7b84aee-monitor   \n12     missing-values-ratio-35881327-monitor   \n13            numerical-drift-monitor-6oxi83   \n14            numerical-drift-monitor-8yugth   \n15  continuous-distribution-956a280c-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n4    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n5    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n6    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n7    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n8    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n9   elated-palegreen-jaguar-6432-analyzer           histogram             9   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11                  unique-ratio-b7b84aee    unique_est_ratio            69   \n12          missing-values-ratio-35881327    count_null_ratio            21   \n13        numerical-drift-analyzer-6oxi83           histogram             1   \n14        numerical-drift-analyzer-8yugth           histogram             1   \n15       continuous-distribution-956a280c           histogram             1   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             34                      30   \n1               1             34                      30   \n2               1             34                      30   \n3               1             34                      30   \n4               1             34                      30   \n5               1             34                      30   \n6               1             34                      30   \n7               1             34                      30   \n8               1             34                      30   \n9               1             75                      19   \n10              1             14                      14   \n11              1            104                       4   \n12              1             27                       3   \n13              1              2                       2   \n14              1              2                       2   \n15              1              2                       2   \n16              1              3                       2   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        1                      11             1   \n1                        1                      11             0   \n2                        1                      11             0   \n3                        1                      11             0   \n4                        1                      11             2   \n5                        1                      11             1   \n6                        1                      11             0   \n7                        1                      11             1   \n8                        1                      11             1   \n9                        2                       8             0   \n10                      14                      14             2   \n11                       1                       1             0   \n12                       1                       1             0   \n13                       2                       2             0   \n14                       2                       2             0   \n15                       2                       2             0   \n16                       1                       1             0   \n\n    action_targets  \n0          [email]  \n1               []  \n2               []  \n3               []  \n4   [email, slack]  \n5          [email]  \n6               []  \n7          [email]  \n8          [email]  \n9               []  \n10  [email, slack]  \n11              []  \n12              []  \n13              []  \n14              []  \n15              []  \n16              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>discrete-distribution-22ef37c9-monitor</td>\n      <td>discrete-distribution-22ef37c9</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>smoggy-chartreuse-owl-3387</td>\n      <td>smoggy-chartreuse-owl-3387-analyzer</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-bx6m80</td>\n      <td>frequent-items-drift-analyzer-bx6m80</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>frequent-items-drift-monitor-0foigt</td>\n      <td>frequent-items-drift-analyzer-0foigt</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>9</td>\n      <td>1</td>\n      <td>75</td>\n      <td>19</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>69</td>\n      <td>1</td>\n      <td>104</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>21</td>\n      <td>1</td>\n      <td>27</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>numerical-drift-monitor-6oxi83</td>\n      <td>numerical-drift-analyzer-6oxi83</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>numerical-drift-monitor-8yugth</td>\n      <td>numerical-drift-analyzer-8yugth</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>continuous-distribution-956a280c-monitor</td>\n      <td>continuous-distribution-956a280c</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>dull-floralwhite-raven-5521</td>\n      <td>dull-floralwhite-raven-5521-analyzer</td>\n      <td>count</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 5,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,12 +181,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.427628Z",
-     "start_time": "2024-05-03T02:03:33.422561Z"
+     "end_time": "2024-05-03T19:28:11.043855Z",
+     "start_time": "2024-05-03T19:28:11.035480Z"
     }
    },
    "outputs": [
@@ -195,7 +195,7 @@
       "text/plain": "                              monitor_id                    analyzer_id  \\\n0    inferred-data-type-fec5a735-monitor    inferred-data-type-fec5a735   \n1  missing-values-ratio-35881327-monitor  missing-values-ratio-35881327   \n2          unique-ratio-b7b84aee-monitor          unique-ratio-b7b84aee   \n\n               metric  failed_count  max_failed_per_column  \\\n0  inferred_data_type             3                      3   \n1    count_null_ratio             1                      1   \n2    unique_est_ratio             1                      1   \n\n   min_failed_per_column  avg_failed_per_column  action_count  action_targets  \n0                      3                      3             2  [email, slack]  \n1                      1                      1             0              []  \n2                      1                      1             0              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>failed_count</th>\n      <th>max_failed_per_column</th>\n      <th>min_failed_per_column</th>\n      <th>avg_failed_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 6,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,11 +214,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 44,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.431118Z",
-     "start_time": "2024-05-03T02:03:33.428470Z"
+     "end_time": "2024-05-03T19:28:11.048310Z",
+     "start_time": "2024-05-03T19:28:11.045233Z"
     }
    },
    "outputs": [
@@ -226,7 +226,7 @@
      "data": {
       "text/plain": "'frequent-items-drift-monitor-x2hr9z'"
      },
-     "execution_count": 7,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,12 +246,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.434461Z",
-     "start_time": "2024-05-03T02:03:33.432056Z"
+     "end_time": "2024-05-03T19:28:11.052828Z",
+     "start_time": "2024-05-03T19:28:11.049460Z"
     }
    },
    "outputs": [
@@ -259,7 +259,7 @@
      "data": {
       "text/plain": "Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')])"
      },
-     "execution_count": 8,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,20 +279,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.623760Z",
-     "start_time": "2024-05-03T02:03:33.435077Z"
+     "end_time": "2024-05-03T19:28:11.245894Z",
+     "start_time": "2024-05-03T19:28:11.054041Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "Analyzer(metadata=Metadata(version=2, schemaVersion=1, updatedTimestamp=1714699900837, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=[<ColumnGroups.group_output: 'group:output'>, 'desc', 'issue_d', 'url'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None)))"
+      "text/plain": "Analyzer(metadata=Metadata(version=5, schemaVersion=1, updatedTimestamp=1714764383143, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['url', 'desc', <ColumnGroups.group_output: 'group:output'>, 'issue_d'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None)))"
      },
-     "execution_count": 9,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,12 +312,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.839587Z",
-     "start_time": "2024-05-03T02:03:33.624489Z"
+     "end_time": "2024-05-03T19:28:11.479972Z",
+     "start_time": "2024-05-03T19:28:11.246810Z"
     }
    },
    "outputs": [
@@ -326,7 +326,7 @@
       "text/plain": "   segment  total_anomalies  batch_count\n0  overall               34           30",
       "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>segment</th>\n      <th>total_anomalies</th>\n      <th>batch_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>overall</td>\n      <td>34</td>\n      <td>30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 10,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,11 +351,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 48,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.846498Z",
-     "start_time": "2024-05-03T02:03:33.842929Z"
+     "end_time": "2024-05-03T19:28:11.486357Z",
+     "start_time": "2024-05-03T19:28:11.483353Z"
     }
    },
    "outputs": [
@@ -363,7 +363,7 @@
      "data": {
       "text/plain": "'overall'"
      },
-     "execution_count": 11,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,11 +383,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 49,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.988624Z",
-     "start_time": "2024-05-03T02:03:33.847364Z"
+     "end_time": "2024-05-03T19:28:11.643588Z",
+     "start_time": "2024-05-03T19:28:11.487234Z"
     }
    },
    "outputs": [
@@ -396,7 +396,7 @@
       "text/plain": "                       column  total_anomalies\n0                     issue_d               30\n1                         url                3\n2                        desc                1\n3         disbursement_method                0\n4            earliest_cr_line                0\n5                  emp_length                0\n6                   emp_title                0\n7                       grade                0\n8               hardship_flag                0\n9              home_ownership                0\n10        initial_list_status                0\n11         last_credit_pull_d                0\n12               last_pymnt_d                0\n13                loan_status                0\n14               next_pymnt_d                0\n15                    purpose                0\n16                 pymnt_plan                0\n17                  sub_grade                0\n18                       term                0\n19                      title                0\n20        verification_status                0\n21  verification_status_joint                0\n22                 addr_state                0\n23                   zip_code                0\n24           application_type                0\n25       debt_settlement_flag                0",
       "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>column</th>\n      <th>total_anomalies</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>issue_d</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>url</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>desc</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>disbursement_method</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>earliest_cr_line</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>emp_length</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>emp_title</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>grade</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>hardship_flag</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>home_ownership</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>initial_list_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>last_credit_pull_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>last_pymnt_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>loan_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>next_pymnt_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>purpose</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>pymnt_plan</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>sub_grade</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>term</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>title</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>verification_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>verification_status_joint</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>addr_state</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>zip_code</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>application_type</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>debt_settlement_flag</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 12,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -416,11 +416,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 50,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:33.992473Z",
-     "start_time": "2024-05-03T02:03:33.989400Z"
+     "end_time": "2024-05-03T19:28:11.647546Z",
+     "start_time": "2024-05-03T19:28:11.644423Z"
     }
    },
    "outputs": [
@@ -428,7 +428,7 @@
      "data": {
       "text/plain": "['issue_d',\n 'url',\n 'desc',\n 'disbursement_method',\n 'earliest_cr_line',\n 'emp_length',\n 'emp_title',\n 'grade',\n 'hardship_flag',\n 'home_ownership',\n 'initial_list_status',\n 'last_credit_pull_d',\n 'last_pymnt_d',\n 'loan_status',\n 'next_pymnt_d',\n 'purpose',\n 'pymnt_plan',\n 'sub_grade',\n 'term',\n 'title',\n 'verification_status',\n 'verification_status_joint',\n 'addr_state',\n 'zip_code',\n 'application_type',\n 'debt_settlement_flag']"
      },
-     "execution_count": 13,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,11 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 51,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:38.883541Z",
-     "start_time": "2024-05-03T02:03:33.993121Z"
+     "end_time": "2024-05-03T19:28:15.881932Z",
+     "start_time": "2024-05-03T19:28:11.648505Z"
     }
    },
    "outputs": [],
@@ -463,12 +463,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T02:03:38.892248Z",
-     "start_time": "2024-05-03T02:03:38.884434Z"
+     "end_time": "2024-05-03T19:28:15.890154Z",
+     "start_time": "2024-05-03T19:28:15.883421Z"
     }
    },
    "outputs": [
diff --git a/examples/example_notebooks/diagnoser.ipynb b/examples/example_notebooks/diagnoser.ipynb
index ff4d687..01d9b06 100644
--- a/examples/example_notebooks/diagnoser.ipynb
+++ b/examples/example_notebooks/diagnoser.ipynb
@@ -18,14 +18,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:30.980544Z",
-     "start_time": "2024-05-03T01:31:30.977943Z"
+     "end_time": "2024-05-03T19:26:07.122244Z",
+     "start_time": "2024-05-03T19:26:07.119688Z"
     }
    },
    "outputs": [],
@@ -48,11 +48,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:33.173740Z",
-     "start_time": "2024-05-03T01:31:30.988187Z"
+     "end_time": "2024-05-03T19:26:15.399941Z",
+     "start_time": "2024-05-03T19:26:07.131180Z"
     }
    },
    "outputs": [],
@@ -62,7 +62,7 @@
     "\n",
     "org_id = input(\"Enter org ID\")\n",
     "dataset_id = input(\"Enter model/dataset ID\")\n",
-    "api_key = getpass.getpass()\n",
+    "api_key = getpass.getpass(\"Enter API key\")\n",
     "api_endpoint = 'https://api.whylabsapp.com'\n",
     "\n",
     "env_setup(\n",
@@ -82,11 +82,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:33.433678Z",
-     "start_time": "2024-05-03T01:31:33.175869Z"
+     "end_time": "2024-05-03T19:26:15.406192Z",
+     "start_time": "2024-05-03T19:26:15.402128Z"
     }
    },
    "outputs": [],
@@ -106,19 +106,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.402540Z",
-     "start_time": "2024-05-03T01:31:33.436496Z"
+     "end_time": "2024-05-03T19:26:22.706626Z",
+     "start_time": "2024-05-03T19:26:15.407624Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "MonitorDiagnosisReport(orgId='org-0', datasetId='model-0', analyzerId='frequent-items-drift-analyzer-x2hr9z', interval='2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z', expectedBatchCount=0, diagnosticData=DiagnosticDataSummary(diagnosticSegment=Segment(tags=[]), diagnosticProfile=ProfileSummary(minRowName='desc', minRowCount=1674392, maxRowName='desc', maxRowCount=1674392), diagnosticBatches=BatchesSummary(minBatchName='desc', minBatchCount=30, maxBatchName='desc', maxBatchCount=30), analysisResults=AnalysisResultsSummary(results=ResultRecord(diagnosedColumnCount=26, batchCount=30), failures=FailureRecord(totalFailuresCount=0, maxFailuresCount=0, meanFailuresCount=0, byColumnCount=[], byTypeCount=[]), anomalies=AnomalyRecord(totalAnomalyCount=34, maxAnomalyCount=30, meanAnomalyCount=11, batchCount=30, byColumnCount=[NamedCount(name='issue_d', count=30), NamedCount(name='url', count=3), NamedCount(name='desc', count=1)], byColumnBatchCount=[NamedCount(name='addr_state', count=30), NamedCount(name='application_type', count=30), NamedCount(name='debt_settlement_flag', count=30), NamedCount(name='desc', count=2), NamedCount(name='disbursement_method', count=30), NamedCount(name='earliest_cr_line', count=30), NamedCount(name='emp_length', count=30), NamedCount(name='emp_title', count=30), NamedCount(name='grade', count=30), NamedCount(name='hardship_flag', count=30), NamedCount(name='home_ownership', count=30), NamedCount(name='initial_list_status', count=30), NamedCount(name='issue_d', count=30), NamedCount(name='last_credit_pull_d', count=30), NamedCount(name='last_pymnt_d', count=30), NamedCount(name='loan_status', count=30), NamedCount(name='next_pymnt_d', count=30), NamedCount(name='purpose', count=30), NamedCount(name='pymnt_plan', count=30), NamedCount(name='sub_grade', count=30), NamedCount(name='term', count=30), NamedCount(name='title', count=30), NamedCount(name='url', count=30), NamedCount(name='verification_status', count=30), NamedCount(name='verification_status_joint', count=30), NamedCount(name='zip_code', count=30)])), targetedColumnCount=30), qualityIssues=[], conditions=[ConditionRecord(columns=['desc', 'issue_d', 'url'], info=None, summary='many values are unique across batches', name='changing_discrete'), ConditionRecord(columns=['desc'], info=None, summary='less than 500 non-null records in 50% or more of the batches', name='small_nonnull_batches')], monitor=Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')]), analyzer=Analyzer(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536888574, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=[<ColumnGroups.group_output: 'group:output'>], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None))), analyzedColumnCount=26)"
+      "text/plain": "MonitorDiagnosisReport(orgId='org-0', datasetId='model-0', analyzerId='frequent-items-drift-analyzer-x2hr9z', interval='2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z', expectedBatchCount=0, diagnosticData=DiagnosticDataSummary(diagnosticSegment=Segment(tags=[]), diagnosticProfile=ProfileSummary(minRowName='desc', minRowCount=1674392, maxRowName='desc', maxRowCount=1674392), diagnosticBatches=BatchesSummary(minBatchName='desc', minBatchCount=30, maxBatchName='desc', maxBatchCount=30), analysisResults=AnalysisResultsSummary(results=ResultRecord(diagnosedColumnCount=26, batchCount=30), failures=FailureRecord(totalFailuresCount=0, maxFailuresCount=0, meanFailuresCount=0, byColumnCount=[], byTypeCount=[]), anomalies=AnomalyRecord(totalAnomalyCount=34, maxAnomalyCount=30, meanAnomalyCount=11, batchCount=30, byColumnCount=[NamedCount(name='issue_d', count=30), NamedCount(name='url', count=3), NamedCount(name='desc', count=1)], byColumnBatchCount=[NamedCount(name='addr_state', count=30), NamedCount(name='application_type', count=30), NamedCount(name='debt_settlement_flag', count=30), NamedCount(name='desc', count=2), NamedCount(name='disbursement_method', count=30), NamedCount(name='earliest_cr_line', count=30), NamedCount(name='emp_length', count=30), NamedCount(name='emp_title', count=30), NamedCount(name='grade', count=30), NamedCount(name='hardship_flag', count=30), NamedCount(name='home_ownership', count=30), NamedCount(name='initial_list_status', count=30), NamedCount(name='issue_d', count=30), NamedCount(name='last_credit_pull_d', count=30), NamedCount(name='last_pymnt_d', count=30), NamedCount(name='loan_status', count=30), NamedCount(name='next_pymnt_d', count=30), NamedCount(name='purpose', count=30), NamedCount(name='pymnt_plan', count=30), NamedCount(name='sub_grade', count=30), NamedCount(name='term', count=30), NamedCount(name='title', count=30), NamedCount(name='url', count=30), NamedCount(name='verification_status', count=30), NamedCount(name='verification_status_joint', count=30), NamedCount(name='zip_code', count=30)])), targetedColumnCount=27), qualityIssues=[QualityIssueRecord(name='analyzer_changed', description='Analyzer changed within the diagnostic interval', detectors=['stale_analysis', 'changing_discrete', 'low_drift_threshold', 'missing_baseline_batches', 'small_nonnull_batches'])], conditions=[ConditionRecord(columns=['desc', 'issue_d', 'url'], info=None, summary='many values are unique across batches', name='changing_discrete'), ConditionRecord(columns=['desc'], info=None, summary='less than 500 non-null records in 50% or more of the batches', name='small_nonnull_batches')], monitor=Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')]), analyzer=Analyzer(metadata=Metadata(version=4, schemaVersion=1, updatedTimestamp=1714763295788, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['desc', <ColumnGroups.group_output: 'group:output'>, 'issue_d', 'url'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None))), analyzedColumnCount=26)"
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,11 +130,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.415056Z",
-     "start_time": "2024-05-03T01:31:40.405237Z"
+     "end_time": "2024-05-03T19:26:22.723005Z",
+     "start_time": "2024-05-03T19:26:22.709411Z"
     }
    },
    "outputs": [
@@ -146,7 +146,7 @@
       "Monitor has 1 notification actions ['email'].\n",
       "\n",
       "Analyzer is drift configuration for frequent_items metric with TrailingWindow baseline.\n",
-      "Analyzer \"frequent-items-drift-analyzer-x2hr9z\" targets 30 columns and ran on 26 columns in the diagnosed segment.\n",
+      "Analyzer \"frequent-items-drift-analyzer-x2hr9z\" targets 27 columns and ran on 26 columns in the diagnosed segment.\n",
       "\n",
       "\n",
       "Diagnostic segment is \"overall\".\n",
@@ -166,18 +166,18 @@
       "\n",
       "No failures were detected.\n",
       "\n",
-      "No issues impacting diagnosis quality were detected\n",
+      "Conditions that may impact diagnosis quality include:\n",
+      "\t* analyzer_changed: Analyzer changed within the diagnostic interval - detectors ['stale_analysis', 'changing_discrete', 'low_drift_threshold', 'missing_baseline_batches', 'small_nonnull_batches']\n",
+      "\n",
       "Conditions that may contribute to noise include:\n",
       "\t* Condition changing_discrete (many values are unique across batches) for 3 columns: ['desc', 'issue_d', 'url']\n",
-      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 4 columns: ['desc', 'issue_d', 'url', 'desc']\n",
+      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 1 columns: ['desc']\n",
       "\n",
       "Anomalies for columns with these conditions:\n",
-      "|         |   0 |\n",
-      "|:--------|----:|\n",
-      "| issue_d |  30 |\n",
-      "| url     |   3 |\n",
-      "| desc    |   1 |\n",
-      "Accounting for 34 anomalies out of 34\n"
+      "|      |   0 |\n",
+      "|:-----|----:|\n",
+      "| desc |   1 |\n",
+      "Accounting for 1 anomalies out of 34\n"
      ]
     }
    ],
@@ -199,11 +199,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.419753Z",
-     "start_time": "2024-05-03T01:31:40.415990Z"
+     "end_time": "2024-05-03T19:26:22.728615Z",
+     "start_time": "2024-05-03T19:26:22.724414Z"
     }
    },
    "outputs": [],
@@ -214,11 +214,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.424467Z",
-     "start_time": "2024-05-03T01:31:40.420491Z"
+     "end_time": "2024-05-03T19:26:22.734842Z",
+     "start_time": "2024-05-03T19:26:22.729870Z"
     }
    },
    "outputs": [
@@ -387,9 +387,21 @@
       "        ]\n",
       "      }\n",
       "    },\n",
-      "    \"targetedColumnCount\": 30\n",
+      "    \"targetedColumnCount\": 27\n",
       "  },\n",
-      "  \"qualityIssues\": [],\n",
+      "  \"qualityIssues\": [\n",
+      "    {\n",
+      "      \"name\": \"analyzer_changed\",\n",
+      "      \"description\": \"Analyzer changed within the diagnostic interval\",\n",
+      "      \"detectors\": [\n",
+      "        \"stale_analysis\",\n",
+      "        \"changing_discrete\",\n",
+      "        \"low_drift_threshold\",\n",
+      "        \"missing_baseline_batches\",\n",
+      "        \"small_nonnull_batches\"\n",
+      "      ]\n",
+      "    }\n",
+      "  ],\n",
       "  \"conditions\": [\n",
       "    {\n",
       "      \"columns\": [\n",
@@ -445,10 +457,10 @@
       "  },\n",
       "  \"analyzer\": {\n",
       "    \"metadata\": {\n",
-      "      \"version\": 1,\n",
+      "      \"version\": 4,\n",
       "      \"schemaVersion\": 1,\n",
-      "      \"updatedTimestamp\": 1705536888574,\n",
-      "      \"author\": \"user_809f777d_3741_4991_8ced_42f09b883ac7\",\n",
+      "      \"updatedTimestamp\": 1714763295788,\n",
+      "      \"author\": \"user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98\",\n",
       "      \"description\": null\n",
       "    },\n",
       "    \"id\": \"frequent-items-drift-analyzer-x2hr9z\",\n",
@@ -472,7 +484,10 @@
       "        \"group:discrete\"\n",
       "      ],\n",
       "      \"exclude\": [\n",
-      "        \"group:output\"\n",
+      "        \"desc\",\n",
+      "        \"group:output\",\n",
+      "        \"issue_d\",\n",
+      "        \"url\"\n",
       "      ],\n",
       "      \"profileId\": null\n",
       "    },\n",
@@ -521,11 +536,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.435769Z",
-     "start_time": "2024-05-03T01:31:40.425350Z"
+     "end_time": "2024-05-03T19:26:22.742280Z",
+     "start_time": "2024-05-03T19:26:22.736043Z"
     }
    },
    "outputs": [
@@ -563,11 +578,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:40.438830Z",
-     "start_time": "2024-05-03T01:31:40.436652Z"
+     "end_time": "2024-05-03T19:26:22.745719Z",
+     "start_time": "2024-05-03T19:26:22.743195Z"
     }
    },
    "outputs": [
@@ -586,11 +601,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:41.510071Z",
-     "start_time": "2024-05-03T01:31:40.439570Z"
+     "end_time": "2024-05-03T19:26:23.502634Z",
+     "start_time": "2024-05-03T19:26:22.746622Z"
     }
    },
    "outputs": [
@@ -625,11 +640,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:31:41.528882Z",
-     "start_time": "2024-05-03T01:31:41.513691Z"
+     "end_time": "2024-05-03T19:26:23.525937Z",
+     "start_time": "2024-05-03T19:26:23.508893Z"
     }
    },
    "outputs": [
@@ -638,7 +653,7 @@
       "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-x2hr9z   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-bx6m80   \n4        frequent-items-drift-monitor-mat0jo   \n5        frequent-items-drift-monitor-01rbfl   \n6        frequent-items-drift-monitor-0foigt   \n7        frequent-items-drift-monitor-3c0hc2   \n8        frequent-items-drift-monitor-9gmtix   \n9               elated-palegreen-jaguar-6432   \n10       inferred-data-type-fec5a735-monitor   \n11             unique-ratio-b7b84aee-monitor   \n12     missing-values-ratio-35881327-monitor   \n13            numerical-drift-monitor-6oxi83   \n14            numerical-drift-monitor-8yugth   \n15  continuous-distribution-956a280c-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n4    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n5    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n6    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n7    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n8    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n9   elated-palegreen-jaguar-6432-analyzer           histogram             9   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11                  unique-ratio-b7b84aee    unique_est_ratio            69   \n12          missing-values-ratio-35881327    count_null_ratio            21   \n13        numerical-drift-analyzer-6oxi83           histogram             1   \n14        numerical-drift-analyzer-8yugth           histogram             1   \n15       continuous-distribution-956a280c           histogram             1   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             34                      30   \n1               1             34                      30   \n2               1             34                      30   \n3               1             34                      30   \n4               1             34                      30   \n5               1             34                      30   \n6               1             34                      30   \n7               1             34                      30   \n8               1             34                      30   \n9               1             75                      19   \n10              1             14                      14   \n11              1            104                       4   \n12              1             27                       3   \n13              1              2                       2   \n14              1              2                       2   \n15              1              2                       2   \n16              1              3                       2   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        1                      11             1   \n1                        1                      11             0   \n2                        1                      11             0   \n3                        1                      11             0   \n4                        1                      11             2   \n5                        1                      11             1   \n6                        1                      11             0   \n7                        1                      11             1   \n8                        1                      11             1   \n9                        2                       8             0   \n10                      14                      14             2   \n11                       1                       1             0   \n12                       1                       1             0   \n13                       2                       2             0   \n14                       2                       2             0   \n15                       2                       2             0   \n16                       1                       1             0   \n\n    action_targets  \n0          [email]  \n1               []  \n2               []  \n3               []  \n4   [email, slack]  \n5          [email]  \n6               []  \n7          [email]  \n8          [email]  \n9               []  \n10  [email, slack]  \n11              []  \n12              []  \n13              []  \n14              []  \n15              []  \n16              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>discrete-distribution-22ef37c9-monitor</td>\n      <td>discrete-distribution-22ef37c9</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>smoggy-chartreuse-owl-3387</td>\n      <td>smoggy-chartreuse-owl-3387-analyzer</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-bx6m80</td>\n      <td>frequent-items-drift-analyzer-bx6m80</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>frequent-items-drift-monitor-0foigt</td>\n      <td>frequent-items-drift-analyzer-0foigt</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>9</td>\n      <td>1</td>\n      <td>75</td>\n      <td>19</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>69</td>\n      <td>1</td>\n      <td>104</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>21</td>\n      <td>1</td>\n      <td>27</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>numerical-drift-monitor-6oxi83</td>\n      <td>numerical-drift-analyzer-6oxi83</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>numerical-drift-monitor-8yugth</td>\n      <td>numerical-drift-analyzer-8yugth</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>continuous-distribution-956a280c-monitor</td>\n      <td>continuous-distribution-956a280c</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>dull-floralwhite-raven-5521</td>\n      <td>dull-floralwhite-raven-5521-analyzer</td>\n      <td>count</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -661,11 +676,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:59:10.186840Z",
-     "start_time": "2024-05-03T01:59:03.667269Z"
+     "end_time": "2024-05-03T19:26:28.850168Z",
+     "start_time": "2024-05-03T19:26:23.527095Z"
     }
    },
    "outputs": [
@@ -702,15 +717,13 @@
       "\n",
       "Conditions that may contribute to noise include:\n",
       "\t* Condition changing_discrete (many values are unique across batches) for 3 columns: ['desc', 'issue_d', 'url']\n",
-      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 4 columns: ['desc', 'issue_d', 'url', 'desc']\n",
+      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 1 columns: ['desc']\n",
       "\n",
       "Anomalies for columns with these conditions:\n",
-      "|         |   0 |\n",
-      "|:--------|----:|\n",
-      "| issue_d |  30 |\n",
-      "| url     |   3 |\n",
-      "| desc    |   1 |\n",
-      "Accounting for 34 anomalies out of 34\n"
+      "|      |   0 |\n",
+      "|:-----|----:|\n",
+      "| desc |   1 |\n",
+      "Accounting for 1 anomalies out of 34\n"
      ]
     }
    ],
@@ -732,12 +745,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T01:59:21.543539Z",
-     "start_time": "2024-05-03T01:59:21.533198Z"
+     "end_time": "2024-05-03T19:26:28.860332Z",
+     "start_time": "2024-05-03T19:26:28.851199Z"
     }
    },
    "outputs": [
@@ -746,7 +759,7 @@
       "text/plain": "                            monitor_id                           analyzer_id  \\\n0  frequent-items-drift-monitor-x2hr9z  frequent-items-drift-analyzer-x2hr9z   \n1  frequent-items-drift-monitor-mat0jo  frequent-items-drift-analyzer-mat0jo   \n2  frequent-items-drift-monitor-01rbfl  frequent-items-drift-analyzer-01rbfl   \n3  frequent-items-drift-monitor-3c0hc2  frequent-items-drift-analyzer-3c0hc2   \n4  frequent-items-drift-monitor-9gmtix  frequent-items-drift-analyzer-9gmtix   \n5  inferred-data-type-fec5a735-monitor           inferred-data-type-fec5a735   \n\n               metric  column_count  segment_count  anomaly_count  \\\n0      frequent_items             3              1             34   \n1      frequent_items             3              1             34   \n2      frequent_items             3              1             34   \n3      frequent_items             3              1             34   \n4      frequent_items             3              1             34   \n5  inferred_data_type             1              1             14   \n\n   max_anomaly_per_column  min_anomaly_per_column  avg_anomaly_per_column  \\\n0                      30                       1                      11   \n1                      30                       1                      11   \n2                      30                       1                      11   \n3                      30                       1                      11   \n4                      30                       1                      11   \n5                      14                      14                      14   \n\n   action_count  action_targets  \n0             1         [email]  \n1             2  [email, slack]  \n2             1         [email]  \n3             1         [email]  \n4             1         [email]  \n5             2  [email, slack]  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
index 6226842..da90c02 100644
--- a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
+++ b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
@@ -187,8 +187,8 @@ def describe_conditions(self) -> str:
         if len(self.conditions) == 0:
             return "No conditions related to noise were detected."
         text = "Conditions that may contribute to noise include:\n"
-        condition_cols: List[str] = []
         for condition in self.conditions:
+            condition_cols: List[str] = []
             text += f"\t* Condition {condition.name} ({condition.summary})"
             if condition.columns is not None:
                 condition_cols += condition.columns

From d212c8b803f8c88d67919364aceeb038b3b949b3 Mon Sep 17 00:00:00 2001
From: Christine Draper <christine@whylabs.ai>
Date: Fri, 3 May 2024 16:45:33 -0400
Subject: [PATCH 2/2] Fix column issue properly

---
 .../customized_diagnoser.ipynb                | 114 +++++++++---------
 examples/example_notebooks/diagnoser.ipynb    | 114 +++++++++---------
 .../diagnoser/models/diagnosis_report.py      |   6 +-
 3 files changed, 119 insertions(+), 115 deletions(-)

diff --git a/examples/example_notebooks/customized_diagnoser.ipynb b/examples/example_notebooks/customized_diagnoser.ipynb
index 77791f6..8268621 100644
--- a/examples/example_notebooks/customized_diagnoser.ipynb
+++ b/examples/example_notebooks/customized_diagnoser.ipynb
@@ -17,14 +17,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 1,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:01.733321Z",
-     "start_time": "2024-05-03T19:28:01.731598Z"
+     "end_time": "2024-05-03T20:44:40.525488Z",
+     "start_time": "2024-05-03T20:44:40.522741Z"
     }
    },
    "outputs": [],
@@ -47,12 +47,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:09.883007Z",
-     "start_time": "2024-05-03T19:28:01.757481Z"
+     "end_time": "2024-05-03T20:44:48.257815Z",
+     "start_time": "2024-05-03T20:44:40.549686Z"
     }
    },
    "outputs": [],
@@ -84,12 +84,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:09.888906Z",
-     "start_time": "2024-05-03T19:28:09.884819Z"
+     "end_time": "2024-05-03T20:44:48.555773Z",
+     "start_time": "2024-05-03T20:44:48.259295Z"
     }
    },
    "outputs": [],
@@ -110,11 +110,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:10.298067Z",
-     "start_time": "2024-05-03T19:28:09.890844Z"
+     "end_time": "2024-05-03T20:44:48.967016Z",
+     "start_time": "2024-05-03T20:44:48.556657Z"
     }
    },
    "outputs": [
@@ -122,7 +122,7 @@
      "data": {
       "text/plain": "(TimeRange(start=datetime.datetime(2021, 5, 20, 0, 0, tzinfo=datetime.timezone.utc), end=datetime.datetime(2024, 5, 2, 21, 0, tzinfo=datetime.timezone.utc)),\n <Granularity.daily: 'daily'>,\n '2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z')"
      },
-     "execution_count": 41,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,12 +143,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.034284Z",
-     "start_time": "2024-05-03T19:28:10.300461Z"
+     "end_time": "2024-05-03T20:44:49.668954Z",
+     "start_time": "2024-05-03T20:44:48.968682Z"
     }
    },
    "outputs": [
@@ -157,7 +157,7 @@
       "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-x2hr9z   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-bx6m80   \n4        frequent-items-drift-monitor-mat0jo   \n5        frequent-items-drift-monitor-01rbfl   \n6        frequent-items-drift-monitor-0foigt   \n7        frequent-items-drift-monitor-3c0hc2   \n8        frequent-items-drift-monitor-9gmtix   \n9               elated-palegreen-jaguar-6432   \n10       inferred-data-type-fec5a735-monitor   \n11             unique-ratio-b7b84aee-monitor   \n12     missing-values-ratio-35881327-monitor   \n13            numerical-drift-monitor-6oxi83   \n14            numerical-drift-monitor-8yugth   \n15  continuous-distribution-956a280c-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n4    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n5    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n6    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n7    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n8    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n9   elated-palegreen-jaguar-6432-analyzer           histogram             9   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11                  unique-ratio-b7b84aee    unique_est_ratio            69   \n12          missing-values-ratio-35881327    count_null_ratio            21   \n13        numerical-drift-analyzer-6oxi83           histogram             1   \n14        numerical-drift-analyzer-8yugth           histogram             1   \n15       continuous-distribution-956a280c           histogram             1   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             34                      30   \n1               1             34                      30   \n2               1             34                      30   \n3               1             34                      30   \n4               1             34                      30   \n5               1             34                      30   \n6               1             34                      30   \n7               1             34                      30   \n8               1             34                      30   \n9               1             75                      19   \n10              1             14                      14   \n11              1            104                       4   \n12              1             27                       3   \n13              1              2                       2   \n14              1              2                       2   \n15              1              2                       2   \n16              1              3                       2   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        1                      11             1   \n1                        1                      11             0   \n2                        1                      11             0   \n3                        1                      11             0   \n4                        1                      11             2   \n5                        1                      11             1   \n6                        1                      11             0   \n7                        1                      11             1   \n8                        1                      11             1   \n9                        2                       8             0   \n10                      14                      14             2   \n11                       1                       1             0   \n12                       1                       1             0   \n13                       2                       2             0   \n14                       2                       2             0   \n15                       2                       2             0   \n16                       1                       1             0   \n\n    action_targets  \n0          [email]  \n1               []  \n2               []  \n3               []  \n4   [email, slack]  \n5          [email]  \n6               []  \n7          [email]  \n8          [email]  \n9               []  \n10  [email, slack]  \n11              []  \n12              []  \n13              []  \n14              []  \n15              []  \n16              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>discrete-distribution-22ef37c9-monitor</td>\n      <td>discrete-distribution-22ef37c9</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>smoggy-chartreuse-owl-3387</td>\n      <td>smoggy-chartreuse-owl-3387-analyzer</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-bx6m80</td>\n      <td>frequent-items-drift-analyzer-bx6m80</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>frequent-items-drift-monitor-0foigt</td>\n      <td>frequent-items-drift-analyzer-0foigt</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>9</td>\n      <td>1</td>\n      <td>75</td>\n      <td>19</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>69</td>\n      <td>1</td>\n      <td>104</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>21</td>\n      <td>1</td>\n      <td>27</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>numerical-drift-monitor-6oxi83</td>\n      <td>numerical-drift-analyzer-6oxi83</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>numerical-drift-monitor-8yugth</td>\n      <td>numerical-drift-analyzer-8yugth</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>continuous-distribution-956a280c-monitor</td>\n      <td>continuous-distribution-956a280c</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>dull-floralwhite-raven-5521</td>\n      <td>dull-floralwhite-raven-5521-analyzer</td>\n      <td>count</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 42,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,12 +181,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.043855Z",
-     "start_time": "2024-05-03T19:28:11.035480Z"
+     "end_time": "2024-05-03T20:44:49.675298Z",
+     "start_time": "2024-05-03T20:44:49.669786Z"
     }
    },
    "outputs": [
@@ -195,7 +195,7 @@
       "text/plain": "                              monitor_id                    analyzer_id  \\\n0    inferred-data-type-fec5a735-monitor    inferred-data-type-fec5a735   \n1  missing-values-ratio-35881327-monitor  missing-values-ratio-35881327   \n2          unique-ratio-b7b84aee-monitor          unique-ratio-b7b84aee   \n\n               metric  failed_count  max_failed_per_column  \\\n0  inferred_data_type             3                      3   \n1    count_null_ratio             1                      1   \n2    unique_est_ratio             1                      1   \n\n   min_failed_per_column  avg_failed_per_column  action_count  action_targets  \n0                      3                      3             2  [email, slack]  \n1                      1                      1             0              []  \n2                      1                      1             0              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>failed_count</th>\n      <th>max_failed_per_column</th>\n      <th>min_failed_per_column</th>\n      <th>avg_failed_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 43,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,11 +214,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.048310Z",
-     "start_time": "2024-05-03T19:28:11.045233Z"
+     "end_time": "2024-05-03T20:44:49.678419Z",
+     "start_time": "2024-05-03T20:44:49.676141Z"
     }
    },
    "outputs": [
@@ -226,7 +226,7 @@
      "data": {
       "text/plain": "'frequent-items-drift-monitor-x2hr9z'"
      },
-     "execution_count": 44,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,12 +246,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.052828Z",
-     "start_time": "2024-05-03T19:28:11.049460Z"
+     "end_time": "2024-05-03T20:44:49.681587Z",
+     "start_time": "2024-05-03T20:44:49.679132Z"
     }
    },
    "outputs": [
@@ -259,7 +259,7 @@
      "data": {
       "text/plain": "Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')])"
      },
-     "execution_count": 45,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,20 +279,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.245894Z",
-     "start_time": "2024-05-03T19:28:11.054041Z"
+     "end_time": "2024-05-03T20:44:49.878456Z",
+     "start_time": "2024-05-03T20:44:49.682343Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "Analyzer(metadata=Metadata(version=5, schemaVersion=1, updatedTimestamp=1714764383143, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['url', 'desc', <ColumnGroups.group_output: 'group:output'>, 'issue_d'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None)))"
+      "text/plain": "Analyzer(metadata=Metadata(version=6, schemaVersion=1, updatedTimestamp=1714769079201, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['issue_d', <ColumnGroups.group_output: 'group:output'>, 'desc', 'url'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None)))"
      },
-     "execution_count": 46,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,12 +312,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.479972Z",
-     "start_time": "2024-05-03T19:28:11.246810Z"
+     "end_time": "2024-05-03T20:44:50.042989Z",
+     "start_time": "2024-05-03T20:44:49.879144Z"
     }
    },
    "outputs": [
@@ -326,7 +326,7 @@
       "text/plain": "   segment  total_anomalies  batch_count\n0  overall               34           30",
       "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>segment</th>\n      <th>total_anomalies</th>\n      <th>batch_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>overall</td>\n      <td>34</td>\n      <td>30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 47,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,11 +351,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.486357Z",
-     "start_time": "2024-05-03T19:28:11.483353Z"
+     "end_time": "2024-05-03T20:44:50.047697Z",
+     "start_time": "2024-05-03T20:44:50.045178Z"
     }
    },
    "outputs": [
@@ -363,7 +363,7 @@
      "data": {
       "text/plain": "'overall'"
      },
-     "execution_count": 48,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,11 +383,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.643588Z",
-     "start_time": "2024-05-03T19:28:11.487234Z"
+     "end_time": "2024-05-03T20:44:50.203811Z",
+     "start_time": "2024-05-03T20:44:50.048446Z"
     }
    },
    "outputs": [
@@ -396,7 +396,7 @@
       "text/plain": "                       column  total_anomalies\n0                     issue_d               30\n1                         url                3\n2                        desc                1\n3         disbursement_method                0\n4            earliest_cr_line                0\n5                  emp_length                0\n6                   emp_title                0\n7                       grade                0\n8               hardship_flag                0\n9              home_ownership                0\n10        initial_list_status                0\n11         last_credit_pull_d                0\n12               last_pymnt_d                0\n13                loan_status                0\n14               next_pymnt_d                0\n15                    purpose                0\n16                 pymnt_plan                0\n17                  sub_grade                0\n18                       term                0\n19                      title                0\n20        verification_status                0\n21  verification_status_joint                0\n22                 addr_state                0\n23                   zip_code                0\n24           application_type                0\n25       debt_settlement_flag                0",
       "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>column</th>\n      <th>total_anomalies</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>issue_d</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>url</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>desc</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>disbursement_method</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>earliest_cr_line</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>emp_length</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>emp_title</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>grade</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>hardship_flag</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>home_ownership</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>initial_list_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>last_credit_pull_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>last_pymnt_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>loan_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>next_pymnt_d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>purpose</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>pymnt_plan</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>sub_grade</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>term</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>title</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>verification_status</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>verification_status_joint</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>addr_state</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>zip_code</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>application_type</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>debt_settlement_flag</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 49,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -416,11 +416,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:11.647546Z",
-     "start_time": "2024-05-03T19:28:11.644423Z"
+     "end_time": "2024-05-03T20:44:50.207865Z",
+     "start_time": "2024-05-03T20:44:50.204706Z"
     }
    },
    "outputs": [
@@ -428,7 +428,7 @@
      "data": {
       "text/plain": "['issue_d',\n 'url',\n 'desc',\n 'disbursement_method',\n 'earliest_cr_line',\n 'emp_length',\n 'emp_title',\n 'grade',\n 'hardship_flag',\n 'home_ownership',\n 'initial_list_status',\n 'last_credit_pull_d',\n 'last_pymnt_d',\n 'loan_status',\n 'next_pymnt_d',\n 'purpose',\n 'pymnt_plan',\n 'sub_grade',\n 'term',\n 'title',\n 'verification_status',\n 'verification_status_joint',\n 'addr_state',\n 'zip_code',\n 'application_type',\n 'debt_settlement_flag']"
      },
-     "execution_count": 50,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,11 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:15.881932Z",
-     "start_time": "2024-05-03T19:28:11.648505Z"
+     "end_time": "2024-05-03T20:44:54.750266Z",
+     "start_time": "2024-05-03T20:44:50.208637Z"
     }
    },
    "outputs": [],
@@ -463,12 +463,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:28:15.890154Z",
-     "start_time": "2024-05-03T19:28:15.883421Z"
+     "end_time": "2024-05-03T20:44:54.758004Z",
+     "start_time": "2024-05-03T20:44:54.751369Z"
     }
    },
    "outputs": [
@@ -505,7 +505,7 @@
       "\n",
       "Conditions that may contribute to noise include:\n",
       "\t* Condition changing_discrete (many values are unique across batches) for 3 columns: ['desc', 'issue_d', 'url']\n",
-      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 4 columns: ['desc', 'issue_d', 'url', 'desc']\n",
+      "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 1 columns: ['desc']\n",
       "\n",
       "Anomalies for columns with these conditions:\n",
       "|         |   0 |\n",
diff --git a/examples/example_notebooks/diagnoser.ipynb b/examples/example_notebooks/diagnoser.ipynb
index 01d9b06..7118071 100644
--- a/examples/example_notebooks/diagnoser.ipynb
+++ b/examples/example_notebooks/diagnoser.ipynb
@@ -18,14 +18,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:07.122244Z",
-     "start_time": "2024-05-03T19:26:07.119688Z"
+     "end_time": "2024-05-03T20:44:23.177189Z",
+     "start_time": "2024-05-03T20:44:23.175308Z"
     }
    },
    "outputs": [],
@@ -48,11 +48,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:15.399941Z",
-     "start_time": "2024-05-03T19:26:07.131180Z"
+     "end_time": "2024-05-03T20:44:30.824990Z",
+     "start_time": "2024-05-03T20:44:23.209328Z"
     }
    },
    "outputs": [],
@@ -82,11 +82,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:15.406192Z",
-     "start_time": "2024-05-03T19:26:15.402128Z"
+     "end_time": "2024-05-03T20:44:31.391534Z",
+     "start_time": "2024-05-03T20:44:30.827501Z"
     }
    },
    "outputs": [],
@@ -106,19 +106,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.706626Z",
-     "start_time": "2024-05-03T19:26:15.407624Z"
+     "end_time": "2024-05-03T20:44:38.796330Z",
+     "start_time": "2024-05-03T20:44:31.392151Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "MonitorDiagnosisReport(orgId='org-0', datasetId='model-0', analyzerId='frequent-items-drift-analyzer-x2hr9z', interval='2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z', expectedBatchCount=0, diagnosticData=DiagnosticDataSummary(diagnosticSegment=Segment(tags=[]), diagnosticProfile=ProfileSummary(minRowName='desc', minRowCount=1674392, maxRowName='desc', maxRowCount=1674392), diagnosticBatches=BatchesSummary(minBatchName='desc', minBatchCount=30, maxBatchName='desc', maxBatchCount=30), analysisResults=AnalysisResultsSummary(results=ResultRecord(diagnosedColumnCount=26, batchCount=30), failures=FailureRecord(totalFailuresCount=0, maxFailuresCount=0, meanFailuresCount=0, byColumnCount=[], byTypeCount=[]), anomalies=AnomalyRecord(totalAnomalyCount=34, maxAnomalyCount=30, meanAnomalyCount=11, batchCount=30, byColumnCount=[NamedCount(name='issue_d', count=30), NamedCount(name='url', count=3), NamedCount(name='desc', count=1)], byColumnBatchCount=[NamedCount(name='addr_state', count=30), NamedCount(name='application_type', count=30), NamedCount(name='debt_settlement_flag', count=30), NamedCount(name='desc', count=2), NamedCount(name='disbursement_method', count=30), NamedCount(name='earliest_cr_line', count=30), NamedCount(name='emp_length', count=30), NamedCount(name='emp_title', count=30), NamedCount(name='grade', count=30), NamedCount(name='hardship_flag', count=30), NamedCount(name='home_ownership', count=30), NamedCount(name='initial_list_status', count=30), NamedCount(name='issue_d', count=30), NamedCount(name='last_credit_pull_d', count=30), NamedCount(name='last_pymnt_d', count=30), NamedCount(name='loan_status', count=30), NamedCount(name='next_pymnt_d', count=30), NamedCount(name='purpose', count=30), NamedCount(name='pymnt_plan', count=30), NamedCount(name='sub_grade', count=30), NamedCount(name='term', count=30), NamedCount(name='title', count=30), NamedCount(name='url', count=30), NamedCount(name='verification_status', count=30), NamedCount(name='verification_status_joint', count=30), NamedCount(name='zip_code', count=30)])), targetedColumnCount=27), qualityIssues=[QualityIssueRecord(name='analyzer_changed', description='Analyzer changed within the diagnostic interval', detectors=['stale_analysis', 'changing_discrete', 'low_drift_threshold', 'missing_baseline_batches', 'small_nonnull_batches'])], conditions=[ConditionRecord(columns=['desc', 'issue_d', 'url'], info=None, summary='many values are unique across batches', name='changing_discrete'), ConditionRecord(columns=['desc'], info=None, summary='less than 500 non-null records in 50% or more of the batches', name='small_nonnull_batches')], monitor=Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')]), analyzer=Analyzer(metadata=Metadata(version=4, schemaVersion=1, updatedTimestamp=1714763295788, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['desc', <ColumnGroups.group_output: 'group:output'>, 'issue_d', 'url'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None))), analyzedColumnCount=26)"
+      "text/plain": "MonitorDiagnosisReport(orgId='org-0', datasetId='model-0', analyzerId='frequent-items-drift-analyzer-x2hr9z', interval='2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z', expectedBatchCount=0, diagnosticData=DiagnosticDataSummary(diagnosticSegment=Segment(tags=[]), diagnosticProfile=ProfileSummary(minRowName='desc', minRowCount=1674392, maxRowName='desc', maxRowCount=1674392), diagnosticBatches=BatchesSummary(minBatchName='desc', minBatchCount=30, maxBatchName='desc', maxBatchCount=30), analysisResults=AnalysisResultsSummary(results=ResultRecord(diagnosedColumnCount=26, batchCount=30), failures=FailureRecord(totalFailuresCount=0, maxFailuresCount=0, meanFailuresCount=0, byColumnCount=[], byTypeCount=[]), anomalies=AnomalyRecord(totalAnomalyCount=34, maxAnomalyCount=30, meanAnomalyCount=11, batchCount=30, byColumnCount=[NamedCount(name='issue_d', count=30), NamedCount(name='url', count=3), NamedCount(name='desc', count=1)], byColumnBatchCount=[NamedCount(name='addr_state', count=30), NamedCount(name='application_type', count=30), NamedCount(name='debt_settlement_flag', count=30), NamedCount(name='desc', count=2), NamedCount(name='disbursement_method', count=30), NamedCount(name='earliest_cr_line', count=30), NamedCount(name='emp_length', count=30), NamedCount(name='emp_title', count=30), NamedCount(name='grade', count=30), NamedCount(name='hardship_flag', count=30), NamedCount(name='home_ownership', count=30), NamedCount(name='initial_list_status', count=30), NamedCount(name='issue_d', count=30), NamedCount(name='last_credit_pull_d', count=30), NamedCount(name='last_pymnt_d', count=30), NamedCount(name='loan_status', count=30), NamedCount(name='next_pymnt_d', count=30), NamedCount(name='purpose', count=30), NamedCount(name='pymnt_plan', count=30), NamedCount(name='sub_grade', count=30), NamedCount(name='term', count=30), NamedCount(name='title', count=30), NamedCount(name='url', count=30), NamedCount(name='verification_status', count=30), NamedCount(name='verification_status_joint', count=30), NamedCount(name='zip_code', count=30)])), targetedColumnCount=27), qualityIssues=[QualityIssueRecord(name='analyzer_changed', description='Analyzer changed within the diagnostic interval', detectors=['stale_analysis', 'changing_discrete', 'low_drift_threshold', 'missing_baseline_batches', 'small_nonnull_batches'])], conditions=[ConditionRecord(columns=['desc', 'issue_d', 'url'], info=None, summary='many values are unique across batches', name='changing_discrete'), ConditionRecord(columns=['desc'], info=None, summary='less than 500 non-null records in 50% or more of the batches', name='small_nonnull_batches')], monitor=Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1705536890090, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-x2hr9z', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-x2hr9z'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[GlobalAction(type='global', target='email')]), analyzer=Analyzer(metadata=Metadata(version=5, schemaVersion=1, updatedTimestamp=1714764383143, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-x2hr9z', displayName=None, tags=None, schedule=FixedCadenceSchedule(type='fixed', cadence=<Cadence.daily: 'daily'>, exclusionRanges=None), disabled=None, disableTargetRollup=None, targetMatrix=ColumnMatrix(segments=[Segment(tags=[])], type=<TargetLevel.column: 'column'>, include=[<ColumnGroups.group_discrete: 'group:discrete'>], exclude=['url', 'desc', <ColumnGroups.group_output: 'group:output'>, 'issue_d'], profileId=None), dataReadinessDuration=None, batchCoolDownPeriod=None, backfillGracePeriodDuration=None, config=DriftConfig(schemaVersion=None, params=None, metric=<ComplexMetrics.frequent_items: 'frequent_items'>, type=<AlgorithmType.drift: 'drift'>, algorithm='hellinger', threshold=0.7, minBatchSize=1, baseline=TrailingWindowBaseline(datasetId=None, inheritSegment=None, type=<BaselineType.TrailingWindow: 'TrailingWindow'>, size=7, offset=None, exclusionRanges=None))), analyzedColumnCount=26)"
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,11 +130,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.723005Z",
-     "start_time": "2024-05-03T19:26:22.709411Z"
+     "end_time": "2024-05-03T20:44:38.811834Z",
+     "start_time": "2024-05-03T20:44:38.798918Z"
     }
    },
    "outputs": [
@@ -174,10 +174,12 @@
       "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 1 columns: ['desc']\n",
       "\n",
       "Anomalies for columns with these conditions:\n",
-      "|      |   0 |\n",
-      "|:-----|----:|\n",
-      "| desc |   1 |\n",
-      "Accounting for 1 anomalies out of 34\n"
+      "|         |   0 |\n",
+      "|:--------|----:|\n",
+      "| issue_d |  30 |\n",
+      "| url     |   3 |\n",
+      "| desc    |   1 |\n",
+      "Accounting for 34 anomalies out of 34\n"
      ]
     }
    ],
@@ -199,11 +201,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.728615Z",
-     "start_time": "2024-05-03T19:26:22.724414Z"
+     "end_time": "2024-05-03T20:44:38.816760Z",
+     "start_time": "2024-05-03T20:44:38.813018Z"
     }
    },
    "outputs": [],
@@ -214,11 +216,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.734842Z",
-     "start_time": "2024-05-03T19:26:22.729870Z"
+     "end_time": "2024-05-03T20:44:38.822615Z",
+     "start_time": "2024-05-03T20:44:38.817801Z"
     }
    },
    "outputs": [
@@ -457,9 +459,9 @@
       "  },\n",
       "  \"analyzer\": {\n",
       "    \"metadata\": {\n",
-      "      \"version\": 4,\n",
+      "      \"version\": 5,\n",
       "      \"schemaVersion\": 1,\n",
-      "      \"updatedTimestamp\": 1714763295788,\n",
+      "      \"updatedTimestamp\": 1714764383143,\n",
       "      \"author\": \"user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98\",\n",
       "      \"description\": null\n",
       "    },\n",
@@ -484,10 +486,10 @@
       "        \"group:discrete\"\n",
       "      ],\n",
       "      \"exclude\": [\n",
+      "        \"url\",\n",
       "        \"desc\",\n",
       "        \"group:output\",\n",
-      "        \"issue_d\",\n",
-      "        \"url\"\n",
+      "        \"issue_d\"\n",
       "      ],\n",
       "      \"profileId\": null\n",
       "    },\n",
@@ -536,11 +538,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.742280Z",
-     "start_time": "2024-05-03T19:26:22.736043Z"
+     "end_time": "2024-05-03T20:44:38.832653Z",
+     "start_time": "2024-05-03T20:44:38.823912Z"
     }
    },
    "outputs": [
@@ -578,11 +580,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:22.745719Z",
-     "start_time": "2024-05-03T19:26:22.743195Z"
+     "end_time": "2024-05-03T20:44:38.835957Z",
+     "start_time": "2024-05-03T20:44:38.833756Z"
     }
    },
    "outputs": [
@@ -601,11 +603,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:23.502634Z",
-     "start_time": "2024-05-03T19:26:22.746622Z"
+     "end_time": "2024-05-03T20:44:39.994817Z",
+     "start_time": "2024-05-03T20:44:38.836984Z"
     }
    },
    "outputs": [
@@ -640,11 +642,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:23.525937Z",
-     "start_time": "2024-05-03T19:26:23.508893Z"
+     "end_time": "2024-05-03T20:44:40.011819Z",
+     "start_time": "2024-05-03T20:44:39.998108Z"
     }
    },
    "outputs": [
@@ -653,7 +655,7 @@
       "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-x2hr9z   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-bx6m80   \n4        frequent-items-drift-monitor-mat0jo   \n5        frequent-items-drift-monitor-01rbfl   \n6        frequent-items-drift-monitor-0foigt   \n7        frequent-items-drift-monitor-3c0hc2   \n8        frequent-items-drift-monitor-9gmtix   \n9               elated-palegreen-jaguar-6432   \n10       inferred-data-type-fec5a735-monitor   \n11             unique-ratio-b7b84aee-monitor   \n12     missing-values-ratio-35881327-monitor   \n13            numerical-drift-monitor-6oxi83   \n14            numerical-drift-monitor-8yugth   \n15  continuous-distribution-956a280c-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n4    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n5    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n6    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n7    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n8    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n9   elated-palegreen-jaguar-6432-analyzer           histogram             9   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11                  unique-ratio-b7b84aee    unique_est_ratio            69   \n12          missing-values-ratio-35881327    count_null_ratio            21   \n13        numerical-drift-analyzer-6oxi83           histogram             1   \n14        numerical-drift-analyzer-8yugth           histogram             1   \n15       continuous-distribution-956a280c           histogram             1   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             34                      30   \n1               1             34                      30   \n2               1             34                      30   \n3               1             34                      30   \n4               1             34                      30   \n5               1             34                      30   \n6               1             34                      30   \n7               1             34                      30   \n8               1             34                      30   \n9               1             75                      19   \n10              1             14                      14   \n11              1            104                       4   \n12              1             27                       3   \n13              1              2                       2   \n14              1              2                       2   \n15              1              2                       2   \n16              1              3                       2   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        1                      11             1   \n1                        1                      11             0   \n2                        1                      11             0   \n3                        1                      11             0   \n4                        1                      11             2   \n5                        1                      11             1   \n6                        1                      11             0   \n7                        1                      11             1   \n8                        1                      11             1   \n9                        2                       8             0   \n10                      14                      14             2   \n11                       1                       1             0   \n12                       1                       1             0   \n13                       2                       2             0   \n14                       2                       2             0   \n15                       2                       2             0   \n16                       1                       1             0   \n\n    action_targets  \n0          [email]  \n1               []  \n2               []  \n3               []  \n4   [email, slack]  \n5          [email]  \n6               []  \n7          [email]  \n8          [email]  \n9               []  \n10  [email, slack]  \n11              []  \n12              []  \n13              []  \n14              []  \n15              []  \n16              []  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>discrete-distribution-22ef37c9-monitor</td>\n      <td>discrete-distribution-22ef37c9</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>smoggy-chartreuse-owl-3387</td>\n      <td>smoggy-chartreuse-owl-3387-analyzer</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-bx6m80</td>\n      <td>frequent-items-drift-analyzer-bx6m80</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>frequent-items-drift-monitor-0foigt</td>\n      <td>frequent-items-drift-analyzer-0foigt</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>9</td>\n      <td>1</td>\n      <td>75</td>\n      <td>19</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>69</td>\n      <td>1</td>\n      <td>104</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>missing-values-ratio-35881327-monitor</td>\n      <td>missing-values-ratio-35881327</td>\n      <td>count_null_ratio</td>\n      <td>21</td>\n      <td>1</td>\n      <td>27</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>numerical-drift-monitor-6oxi83</td>\n      <td>numerical-drift-analyzer-6oxi83</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>numerical-drift-monitor-8yugth</td>\n      <td>numerical-drift-analyzer-8yugth</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>continuous-distribution-956a280c-monitor</td>\n      <td>continuous-distribution-956a280c</td>\n      <td>histogram</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>dull-floralwhite-raven-5521</td>\n      <td>dull-floralwhite-raven-5521-analyzer</td>\n      <td>count</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -676,11 +678,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:28.850168Z",
-     "start_time": "2024-05-03T19:26:23.527095Z"
+     "end_time": "2024-05-03T20:44:45.451258Z",
+     "start_time": "2024-05-03T20:44:40.012742Z"
     }
    },
    "outputs": [
@@ -720,10 +722,12 @@
       "\t* Condition small_nonnull_batches (less than 500 non-null records in 50% or more of the batches) for 1 columns: ['desc']\n",
       "\n",
       "Anomalies for columns with these conditions:\n",
-      "|      |   0 |\n",
-      "|:-----|----:|\n",
-      "| desc |   1 |\n",
-      "Accounting for 1 anomalies out of 34\n"
+      "|         |   0 |\n",
+      "|:--------|----:|\n",
+      "| issue_d |  30 |\n",
+      "| url     |   3 |\n",
+      "| desc    |   1 |\n",
+      "Accounting for 34 anomalies out of 34\n"
      ]
     }
    ],
@@ -745,12 +749,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T19:26:28.860332Z",
-     "start_time": "2024-05-03T19:26:28.851199Z"
+     "end_time": "2024-05-03T20:44:45.461470Z",
+     "start_time": "2024-05-03T20:44:45.452512Z"
     }
    },
    "outputs": [
@@ -759,7 +763,7 @@
       "text/plain": "                            monitor_id                           analyzer_id  \\\n0  frequent-items-drift-monitor-x2hr9z  frequent-items-drift-analyzer-x2hr9z   \n1  frequent-items-drift-monitor-mat0jo  frequent-items-drift-analyzer-mat0jo   \n2  frequent-items-drift-monitor-01rbfl  frequent-items-drift-analyzer-01rbfl   \n3  frequent-items-drift-monitor-3c0hc2  frequent-items-drift-analyzer-3c0hc2   \n4  frequent-items-drift-monitor-9gmtix  frequent-items-drift-analyzer-9gmtix   \n5  inferred-data-type-fec5a735-monitor           inferred-data-type-fec5a735   \n\n               metric  column_count  segment_count  anomaly_count  \\\n0      frequent_items             3              1             34   \n1      frequent_items             3              1             34   \n2      frequent_items             3              1             34   \n3      frequent_items             3              1             34   \n4      frequent_items             3              1             34   \n5  inferred_data_type             1              1             14   \n\n   max_anomaly_per_column  min_anomaly_per_column  avg_anomaly_per_column  \\\n0                      30                       1                      11   \n1                      30                       1                      11   \n2                      30                       1                      11   \n3                      30                       1                      11   \n4                      30                       1                      11   \n5                      14                      14                      14   \n\n   action_count  action_targets  \n0             1         [email]  \n1             2  [email, slack]  \n2             1         [email]  \n3             1         [email]  \n4             1         [email]  \n5             2  [email, slack]  ",
       "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>monitor_id</th>\n      <th>analyzer_id</th>\n      <th>metric</th>\n      <th>column_count</th>\n      <th>segment_count</th>\n      <th>anomaly_count</th>\n      <th>max_anomaly_per_column</th>\n      <th>min_anomaly_per_column</th>\n      <th>avg_anomaly_per_column</th>\n      <th>action_count</th>\n      <th>action_targets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>frequent-items-drift-monitor-x2hr9z</td>\n      <td>frequent-items-drift-analyzer-x2hr9z</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>frequent-items-drift-monitor-mat0jo</td>\n      <td>frequent-items-drift-analyzer-mat0jo</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>frequent-items-drift-monitor-01rbfl</td>\n      <td>frequent-items-drift-analyzer-01rbfl</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>frequent-items-drift-monitor-3c0hc2</td>\n      <td>frequent-items-drift-analyzer-3c0hc2</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>frequent-items-drift-monitor-9gmtix</td>\n      <td>frequent-items-drift-analyzer-9gmtix</td>\n      <td>frequent_items</td>\n      <td>3</td>\n      <td>1</td>\n      <td>34</td>\n      <td>30</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>inferred-data-type-fec5a735-monitor</td>\n      <td>inferred-data-type-fec5a735</td>\n      <td>inferred_data_type</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>14</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
index da90c02..20a6d24 100644
--- a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
+++ b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
@@ -186,14 +186,14 @@ def describe_quality_issues(self) -> str:
     def describe_conditions(self) -> str:
         if len(self.conditions) == 0:
             return "No conditions related to noise were detected."
+        condition_cols: List[str] = []
         text = "Conditions that may contribute to noise include:\n"
         for condition in self.conditions:
-            condition_cols: List[str] = []
             text += f"\t* Condition {condition.name} ({condition.summary})"
             if condition.columns is not None:
                 condition_cols += condition.columns
-                col_text = describe_truncated_list(condition_cols, 10)
-                text += f" for {len(condition_cols)} columns: {col_text}"
+                col_text = describe_truncated_list(condition.columns, 10)
+                text += f" for {len(condition.columns)} columns: {col_text}"
             text += "\n"
 
         cols = pd.Series(condition_cols).unique()