From 4a4d44a86c0c309b9d16b2bc68ed84f94d1f3abf Mon Sep 17 00:00:00 2001
From: Christine Draper <christine@whylabs.ai>
Date: Tue, 14 May 2024 15:54:39 -0400
Subject: [PATCH 1/2] Fix some formatting issues with tables in text diagnoser
 report

---
 .../customized_diagnoser.ipynb                | 163 ++++++++-------
 examples/example_notebooks/diagnoser.ipynb    | 194 +++++++++---------
 .../diagnoser/models/diagnosis_report.py      |   9 +-
 3 files changed, 180 insertions(+), 186 deletions(-)

diff --git a/examples/example_notebooks/customized_diagnoser.ipynb b/examples/example_notebooks/customized_diagnoser.ipynb
index 8268621..4f039c5 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": 20,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:40.525488Z",
-     "start_time": "2024-05-03T20:44:40.522741Z"
+     "end_time": "2024-05-14T19:56:20.461774Z",
+     "start_time": "2024-05-14T19:56:20.459107Z"
     }
    },
    "outputs": [],
@@ -47,12 +47,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:48.257815Z",
-     "start_time": "2024-05-03T20:44:40.549686Z"
+     "end_time": "2024-05-14T19:56:26.198940Z",
+     "start_time": "2024-05-14T19:56:20.491315Z"
     }
    },
    "outputs": [],
@@ -84,12 +84,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:48.555773Z",
-     "start_time": "2024-05-03T20:44:48.259295Z"
+     "end_time": "2024-05-14T19:56:26.209383Z",
+     "start_time": "2024-05-14T19:56:26.201460Z"
     }
    },
    "outputs": [],
@@ -110,19 +110,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:48.967016Z",
-     "start_time": "2024-05-03T20:44:48.556657Z"
+     "end_time": "2024-05-14T19:56:26.626871Z",
+     "start_time": "2024-05-14T19:56:26.210469Z"
     }
    },
    "outputs": [
     {
      "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')"
+      "text/plain": "(TimeRange(start=datetime.datetime(2021, 5, 20, 0, 0, tzinfo=datetime.timezone.utc), end=datetime.datetime(2024, 5, 13, 21, 0, tzinfo=datetime.timezone.utc)),\n <Granularity.daily: 'daily'>,\n '2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z')"
      },
-     "execution_count": 4,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,21 +143,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:49.668954Z",
-     "start_time": "2024-05-03T20:44:48.968682Z"
+     "end_time": "2024-05-14T19:56:27.372526Z",
+     "start_time": "2024-05-14T19:56:26.629839Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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>"
+      "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-bx6m80   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-mat0jo   \n4        frequent-items-drift-monitor-01rbfl   \n5        frequent-items-drift-monitor-0foigt   \n6        frequent-items-drift-monitor-3c0hc2   \n7        frequent-items-drift-monitor-9gmtix   \n8               elated-palegreen-jaguar-6432   \n9        frequent-items-drift-monitor-x2hr9z   \n10       inferred-data-type-fec5a735-monitor   \n11  continuous-distribution-956a280c-monitor   \n12            numerical-drift-monitor-6oxi83   \n13            numerical-drift-monitor-8yugth   \n14     missing-values-ratio-35881327-monitor   \n15             unique-ratio-b7b84aee-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n4    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n5    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n6    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n7    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n8   elated-palegreen-jaguar-6432-analyzer           histogram             8   \n9    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11       continuous-distribution-956a280c           histogram             1   \n12        numerical-drift-analyzer-6oxi83           histogram             1   \n13        numerical-drift-analyzer-8yugth           histogram             1   \n14          missing-values-ratio-35881327    count_null_ratio            21   \n15                  unique-ratio-b7b84aee    unique_est_ratio            68   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             38                      30   \n1               1             38                      30   \n2               1             38                      30   \n3               1             38                      30   \n4               1             38                      30   \n5               1             38                      30   \n6               1             38                      30   \n7               1             38                      30   \n8               1             81                      25   \n9               1             22                      19   \n10              1             18                      18   \n11              1              8                       8   \n12              1              8                       8   \n13              1              8                       8   \n14              1             30                       4   \n15              1            103                       3   \n16              1              2                       1   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        2                      12             0   \n1                        2                      12             0   \n2                        2                      12             0   \n3                        2                      12             2   \n4                        2                      12             1   \n5                        2                      12             0   \n6                        2                      12             1   \n7                        2                      12             1   \n8                        2                      10             0   \n9                        1                       7             1   \n10                      18                      18             2   \n11                       8                       8             0   \n12                       8                       8             0   \n13                       8                       8             0   \n14                       1                       1             0   \n15                       1                       1             0   \n16                       1                       1             0   \n\n    action_targets  \n0               []  \n1               []  \n2               []  \n3   [email, slack]  \n4          [email]  \n5               []  \n6          [email]  \n7          [email]  \n8               []  \n9          [email]  \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-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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>4</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>5</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>6</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>7</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>8</td>\n      <td>1</td>\n      <td>81</td>\n      <td>25</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>9</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>22</td>\n      <td>19</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>[email]</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>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</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>30</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>15</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>68</td>\n      <td>1</td>\n      <td>103</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>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>2</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": 5,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,12 +181,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:49.675298Z",
-     "start_time": "2024-05-03T20:44:49.669786Z"
+     "end_time": "2024-05-14T19:56:27.381587Z",
+     "start_time": "2024-05-14T19:56:27.374413Z"
     }
    },
    "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": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,19 +214,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:49.678419Z",
-     "start_time": "2024-05-03T20:44:49.676141Z"
+     "end_time": "2024-05-14T19:56:27.385574Z",
+     "start_time": "2024-05-14T19:56:27.382543Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "'frequent-items-drift-monitor-x2hr9z'"
+      "text/plain": "'frequent-items-drift-monitor-bx6m80'"
      },
-     "execution_count": 7,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,20 +246,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:49.681587Z",
-     "start_time": "2024-05-03T20:44:49.679132Z"
+     "end_time": "2024-05-14T19:56:27.389072Z",
+     "start_time": "2024-05-14T19:56:27.386612Z"
     }
    },
    "outputs": [
     {
      "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')])"
+      "text/plain": "Monitor(metadata=Metadata(version=1, schemaVersion=1, updatedTimestamp=1711135588156, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-bx6m80', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-bx6m80'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[])"
      },
-     "execution_count": 8,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,20 +279,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:49.878456Z",
-     "start_time": "2024-05-03T20:44:49.682343Z"
+     "end_time": "2024-05-14T19:56:27.584900Z",
+     "start_time": "2024-05-14T19:56:27.389808Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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)))"
+      "text/plain": "Analyzer(metadata=Metadata(version=3, schemaVersion=1, updatedTimestamp=1715716552766, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-bx6m80', 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', <ColumnGroups.group_output: 'group:output'>, 'desc', '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": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,21 +312,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:50.042989Z",
-     "start_time": "2024-05-03T20:44:49.879144Z"
+     "end_time": "2024-05-14T19:56:27.805923Z",
+     "start_time": "2024-05-14T19:56:27.585634Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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>"
+      "text/plain": "   segment  total_anomalies  batch_count\n0  overall               38           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>38</td>\n      <td>30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 10,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,11 +351,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:50.047697Z",
-     "start_time": "2024-05-03T20:44:50.045178Z"
+     "end_time": "2024-05-14T19:56:27.812129Z",
+     "start_time": "2024-05-14T19:56:27.809188Z"
     }
    },
    "outputs": [
@@ -363,7 +363,7 @@
      "data": {
       "text/plain": "'overall'"
      },
-     "execution_count": 11,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,20 +383,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:50.203811Z",
-     "start_time": "2024-05-03T20:44:50.048446Z"
+     "end_time": "2024-05-14T19:56:27.963449Z",
+     "start_time": "2024-05-14T19:56:27.813404Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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>"
+      "text/plain": "                       column  total_anomalies\n0                     issue_d               30\n1                         url                6\n2                        desc                2\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>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>desc</td>\n      <td>2</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": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -416,11 +416,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:50.207865Z",
-     "start_time": "2024-05-03T20:44:50.204706Z"
+     "end_time": "2024-05-14T19:56:27.966888Z",
+     "start_time": "2024-05-14T19:56:27.964239Z"
     }
    },
    "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": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,11 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:54.750266Z",
-     "start_time": "2024-05-03T20:44:50.208637Z"
+     "end_time": "2024-05-14T19:56:32.346779Z",
+     "start_time": "2024-05-14T19:56:27.967796Z"
     }
    },
    "outputs": [],
@@ -463,12 +463,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:54.758004Z",
-     "start_time": "2024-05-03T20:44:54.751369Z"
+     "end_time": "2024-05-14T19:56:32.355851Z",
+     "start_time": "2024-05-14T19:56:32.347857Z"
     }
    },
    "outputs": [
@@ -476,27 +476,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Diagnosis is for monitor \"frequent-items-drift-monitor-x2hr9z\" [frequent-items-drift-monitor-x2hr9z] in model-0 org-0, over interval 2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z.\n",
-      "Monitor has 1 notification actions ['email'].\n",
+      "Diagnosis is for monitor \"frequent-items-drift-monitor-bx6m80\" [frequent-items-drift-monitor-bx6m80] in model-0 org-0, over interval 2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z.\n",
       "\n",
       "Analyzer is drift configuration for frequent_items metric with TrailingWindow baseline.\n",
-      "Analyzer \"frequent-items-drift-analyzer-x2hr9z\" targets 27 columns and ran on 26 columns in the diagnosed segment.\n",
+      "Analyzer \"frequent-items-drift-analyzer-bx6m80\" targets 27 columns and ran on 26 columns in the diagnosed segment.\n",
       "\n",
       "\n",
       "Diagnostic segment is \"overall\".\n",
       "Diagnostic interval contains 30 batches.\n",
       "\n",
-      "Diagnostic interval rollup contains 1674392 rows for the diagnosed columns.\n",
+      "Diagnostic interval rollup contains 1517489 rows for the diagnosed columns.\n",
       "\n",
       "Analysis results summary:\n",
       "Found non-failed results for 26 columns and 30 batches.\n",
-      "Found 34 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 36.7% (11.0) on average.\n",
+      "Found 38 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 40.0% (12.0) on average.\n",
       "Columns with anomalies are:\n",
-      "|    | 0               |\n",
-      "|---:|:----------------|\n",
-      "|  0 | ('issue_d', 30) |\n",
-      "|  1 | ('url', 3)      |\n",
-      "|  2 | ('desc', 1)     |\n",
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
       "\n",
       "No failures were detected.\n",
       "\n",
@@ -508,12 +507,12 @@
       "\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",
-      "| issue_d |  30 |\n",
-      "| url     |   3 |\n",
-      "| desc    |   1 |\n",
-      "Accounting for 34 anomalies out of 34\n"
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
+      "Accounting for 38 anomalies out of 38\n"
      ]
     }
    ],
@@ -538,7 +537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/example_notebooks/diagnoser.ipynb b/examples/example_notebooks/diagnoser.ipynb
index 7118071..a1fdfcd 100644
--- a/examples/example_notebooks/diagnoser.ipynb
+++ b/examples/example_notebooks/diagnoser.ipynb
@@ -18,14 +18,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:23.177189Z",
-     "start_time": "2024-05-03T20:44:23.175308Z"
+     "end_time": "2024-05-14T19:55:38.467963Z",
+     "start_time": "2024-05-14T19:55:38.465132Z"
     }
    },
    "outputs": [],
@@ -48,11 +48,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:30.824990Z",
-     "start_time": "2024-05-03T20:44:23.209328Z"
+     "end_time": "2024-05-14T19:55:44.329405Z",
+     "start_time": "2024-05-14T19:55:38.475473Z"
     }
    },
    "outputs": [],
@@ -82,11 +82,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:31.391534Z",
-     "start_time": "2024-05-03T20:44:30.827501Z"
+     "end_time": "2024-05-14T19:55:44.606466Z",
+     "start_time": "2024-05-14T19:55:44.332079Z"
     }
    },
    "outputs": [],
@@ -106,19 +106,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.796330Z",
-     "start_time": "2024-05-03T20:44:31.392151Z"
+     "end_time": "2024-05-14T19:55:52.358526Z",
+     "start_time": "2024-05-14T19:55:44.609704Z"
     }
    },
    "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=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)"
+      "text/plain": "MonitorDiagnosisReport(orgId='org-0', datasetId='model-0', analyzerId='frequent-items-drift-analyzer-bx6m80', interval='2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z', expectedBatchCount=0, diagnosticData=DiagnosticDataSummary(diagnosticSegment=Segment(tags=[]), diagnosticProfile=ProfileSummary(minRowName='desc', minRowCount=1517489, maxRowName='desc', maxRowCount=1517489), 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=38, maxAnomalyCount=30, meanAnomalyCount=12, batchCount=30, byColumnCount=[NamedCount(name='issue_d', count=30), NamedCount(name='url', count=6), NamedCount(name='desc', count=2)], byColumnBatchCount=[NamedCount(name='addr_state', count=30), NamedCount(name='application_type', count=30), NamedCount(name='debt_settlement_flag', count=30), NamedCount(name='desc', count=3), 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=1711135588156, author='user_809f777d_3741_4991_8ced_42f09b883ac7', description=None), id='frequent-items-drift-monitor-bx6m80', displayName=None, tags=None, analyzerIds=['frequent-items-drift-analyzer-bx6m80'], schedule=ImmediateSchedule(type='immediate'), disabled=False, severity=3, mode=DigestMode(type='DIGEST', filter=None, creationTimeOffset=None, datasetTimestampOffset=None, groupBy=None), actions=[]), analyzer=Analyzer(metadata=Metadata(version=2, schemaVersion=1, updatedTimestamp=1715605098048, author='user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98', description=None), id='frequent-items-drift-analyzer-bx6m80', 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', <ColumnGroups.group_output: 'group:output'>, 'desc', '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": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,11 +130,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.811834Z",
-     "start_time": "2024-05-03T20:44:38.798918Z"
+     "end_time": "2024-05-14T19:55:52.375039Z",
+     "start_time": "2024-05-14T19:55:52.361642Z"
     }
    },
    "outputs": [
@@ -142,27 +142,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Diagnosis is for monitor \"frequent-items-drift-monitor-x2hr9z\" [frequent-items-drift-monitor-x2hr9z] in model-0 org-0, over interval 2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z.\n",
-      "Monitor has 1 notification actions ['email'].\n",
+      "Diagnosis is for monitor \"frequent-items-drift-monitor-bx6m80\" [frequent-items-drift-monitor-bx6m80] in model-0 org-0, over interval 2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z.\n",
       "\n",
       "Analyzer is drift configuration for frequent_items metric with TrailingWindow baseline.\n",
-      "Analyzer \"frequent-items-drift-analyzer-x2hr9z\" targets 27 columns and ran on 26 columns in the diagnosed segment.\n",
+      "Analyzer \"frequent-items-drift-analyzer-bx6m80\" targets 27 columns and ran on 26 columns in the diagnosed segment.\n",
       "\n",
       "\n",
       "Diagnostic segment is \"overall\".\n",
       "Diagnostic interval contains 30 batches.\n",
       "\n",
-      "Diagnostic interval rollup contains 1674392 rows for the diagnosed columns.\n",
+      "Diagnostic interval rollup contains 1517489 rows for the diagnosed columns.\n",
       "\n",
       "Analysis results summary:\n",
       "Found non-failed results for 26 columns and 30 batches.\n",
-      "Found 34 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 36.7% (11.0) on average.\n",
+      "Found 38 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 40.0% (12.0) on average.\n",
       "Columns with anomalies are:\n",
-      "|    | 0               |\n",
-      "|---:|:----------------|\n",
-      "|  0 | ('issue_d', 30) |\n",
-      "|  1 | ('url', 3)      |\n",
-      "|  2 | ('desc', 1)     |\n",
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
       "\n",
       "No failures were detected.\n",
       "\n",
@@ -174,12 +173,12 @@
       "\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",
-      "| issue_d |  30 |\n",
-      "| url     |   3 |\n",
-      "| desc    |   1 |\n",
-      "Accounting for 34 anomalies out of 34\n"
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
+      "Accounting for 38 anomalies out of 38\n"
      ]
     }
    ],
@@ -201,11 +200,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.816760Z",
-     "start_time": "2024-05-03T20:44:38.813018Z"
+     "end_time": "2024-05-14T19:55:52.379621Z",
+     "start_time": "2024-05-14T19:55:52.376215Z"
     }
    },
    "outputs": [],
@@ -216,11 +215,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.822615Z",
-     "start_time": "2024-05-03T20:44:38.817801Z"
+     "end_time": "2024-05-14T19:55:52.384784Z",
+     "start_time": "2024-05-14T19:55:52.380668Z"
     }
    },
    "outputs": [
@@ -231,8 +230,8 @@
       "{\n",
       "  \"orgId\": \"org-0\",\n",
       "  \"datasetId\": \"model-0\",\n",
-      "  \"analyzerId\": \"frequent-items-drift-analyzer-x2hr9z\",\n",
-      "  \"interval\": \"2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z\",\n",
+      "  \"analyzerId\": \"frequent-items-drift-analyzer-bx6m80\",\n",
+      "  \"interval\": \"2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z\",\n",
       "  \"expectedBatchCount\": 0,\n",
       "  \"diagnosticData\": {\n",
       "    \"diagnosticSegment\": {\n",
@@ -240,9 +239,9 @@
       "    },\n",
       "    \"diagnosticProfile\": {\n",
       "      \"minRowName\": \"desc\",\n",
-      "      \"minRowCount\": 1674392,\n",
+      "      \"minRowCount\": 1517489,\n",
       "      \"maxRowName\": \"desc\",\n",
-      "      \"maxRowCount\": 1674392\n",
+      "      \"maxRowCount\": 1517489\n",
       "    },\n",
       "    \"diagnosticBatches\": {\n",
       "      \"minBatchName\": \"desc\",\n",
@@ -263,9 +262,9 @@
       "        \"byTypeCount\": []\n",
       "      },\n",
       "      \"anomalies\": {\n",
-      "        \"totalAnomalyCount\": 34,\n",
+      "        \"totalAnomalyCount\": 38,\n",
       "        \"maxAnomalyCount\": 30,\n",
-      "        \"meanAnomalyCount\": 11,\n",
+      "        \"meanAnomalyCount\": 12,\n",
       "        \"batchCount\": 30,\n",
       "        \"byColumnCount\": [\n",
       "          {\n",
@@ -274,11 +273,11 @@
       "          },\n",
       "          {\n",
       "            \"name\": \"url\",\n",
-      "            \"count\": 3\n",
+      "            \"count\": 6\n",
       "          },\n",
       "          {\n",
       "            \"name\": \"desc\",\n",
-      "            \"count\": 1\n",
+      "            \"count\": 2\n",
       "          }\n",
       "        ],\n",
       "        \"byColumnBatchCount\": [\n",
@@ -296,7 +295,7 @@
       "          },\n",
       "          {\n",
       "            \"name\": \"desc\",\n",
-      "            \"count\": 2\n",
+      "            \"count\": 3\n",
       "          },\n",
       "          {\n",
       "            \"name\": \"disbursement_method\",\n",
@@ -428,15 +427,15 @@
       "    \"metadata\": {\n",
       "      \"version\": 1,\n",
       "      \"schemaVersion\": 1,\n",
-      "      \"updatedTimestamp\": 1705536890090,\n",
+      "      \"updatedTimestamp\": 1711135588156,\n",
       "      \"author\": \"user_809f777d_3741_4991_8ced_42f09b883ac7\",\n",
       "      \"description\": null\n",
       "    },\n",
-      "    \"id\": \"frequent-items-drift-monitor-x2hr9z\",\n",
+      "    \"id\": \"frequent-items-drift-monitor-bx6m80\",\n",
       "    \"displayName\": null,\n",
       "    \"tags\": null,\n",
       "    \"analyzerIds\": [\n",
-      "      \"frequent-items-drift-analyzer-x2hr9z\"\n",
+      "      \"frequent-items-drift-analyzer-bx6m80\"\n",
       "    ],\n",
       "    \"schedule\": {\n",
       "      \"type\": \"immediate\"\n",
@@ -450,22 +449,17 @@
       "      \"datasetTimestampOffset\": null,\n",
       "      \"groupBy\": null\n",
       "    },\n",
-      "    \"actions\": [\n",
-      "      {\n",
-      "        \"type\": \"global\",\n",
-      "        \"target\": \"email\"\n",
-      "      }\n",
-      "    ]\n",
+      "    \"actions\": []\n",
       "  },\n",
       "  \"analyzer\": {\n",
       "    \"metadata\": {\n",
-      "      \"version\": 5,\n",
+      "      \"version\": 2,\n",
       "      \"schemaVersion\": 1,\n",
-      "      \"updatedTimestamp\": 1714764383143,\n",
+      "      \"updatedTimestamp\": 1715605098048,\n",
       "      \"author\": \"user_c9292ec40407f7b580f0a2c90745ebfba2b9e6ea81c848ef944d31e48a45f98\",\n",
       "      \"description\": null\n",
       "    },\n",
-      "    \"id\": \"frequent-items-drift-analyzer-x2hr9z\",\n",
+      "    \"id\": \"frequent-items-drift-analyzer-bx6m80\",\n",
       "    \"displayName\": null,\n",
       "    \"tags\": null,\n",
       "    \"schedule\": {\n",
@@ -487,8 +481,8 @@
       "      ],\n",
       "      \"exclude\": [\n",
       "        \"url\",\n",
-      "        \"desc\",\n",
       "        \"group:output\",\n",
+      "        \"desc\",\n",
       "        \"issue_d\"\n",
       "      ],\n",
       "      \"profileId\": null\n",
@@ -538,11 +532,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.832653Z",
-     "start_time": "2024-05-03T20:44:38.823912Z"
+     "end_time": "2024-05-14T19:55:52.392694Z",
+     "start_time": "2024-05-14T19:55:52.385763Z"
     }
    },
    "outputs": [
@@ -580,11 +574,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:38.835957Z",
-     "start_time": "2024-05-03T20:44:38.833756Z"
+     "end_time": "2024-05-14T19:55:52.395956Z",
+     "start_time": "2024-05-14T19:55:52.393579Z"
     }
    },
    "outputs": [
@@ -603,11 +597,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:39.994817Z",
-     "start_time": "2024-05-03T20:44:38.836984Z"
+     "end_time": "2024-05-14T19:55:53.427942Z",
+     "start_time": "2024-05-14T19:55:52.396796Z"
     }
    },
    "outputs": [
@@ -642,20 +636,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:40.011819Z",
-     "start_time": "2024-05-03T20:44:39.998108Z"
+     "end_time": "2024-05-14T19:55:53.449819Z",
+     "start_time": "2024-05-14T19:55:53.431999Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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>"
+      "text/plain": "                                  monitor_id  \\\n0        frequent-items-drift-monitor-bx6m80   \n1     discrete-distribution-22ef37c9-monitor   \n2                 smoggy-chartreuse-owl-3387   \n3        frequent-items-drift-monitor-mat0jo   \n4        frequent-items-drift-monitor-01rbfl   \n5        frequent-items-drift-monitor-0foigt   \n6        frequent-items-drift-monitor-3c0hc2   \n7        frequent-items-drift-monitor-9gmtix   \n8               elated-palegreen-jaguar-6432   \n9        frequent-items-drift-monitor-x2hr9z   \n10       inferred-data-type-fec5a735-monitor   \n11  continuous-distribution-956a280c-monitor   \n12            numerical-drift-monitor-6oxi83   \n13            numerical-drift-monitor-8yugth   \n14     missing-values-ratio-35881327-monitor   \n15             unique-ratio-b7b84aee-monitor   \n16               dull-floralwhite-raven-5521   \n\n                              analyzer_id              metric  column_count  \\\n0    frequent-items-drift-analyzer-bx6m80      frequent_items             3   \n1          discrete-distribution-22ef37c9      frequent_items             3   \n2     smoggy-chartreuse-owl-3387-analyzer      frequent_items             3   \n3    frequent-items-drift-analyzer-mat0jo      frequent_items             3   \n4    frequent-items-drift-analyzer-01rbfl      frequent_items             3   \n5    frequent-items-drift-analyzer-0foigt      frequent_items             3   \n6    frequent-items-drift-analyzer-3c0hc2      frequent_items             3   \n7    frequent-items-drift-analyzer-9gmtix      frequent_items             3   \n8   elated-palegreen-jaguar-6432-analyzer           histogram             8   \n9    frequent-items-drift-analyzer-x2hr9z      frequent_items             3   \n10            inferred-data-type-fec5a735  inferred_data_type             1   \n11       continuous-distribution-956a280c           histogram             1   \n12        numerical-drift-analyzer-6oxi83           histogram             1   \n13        numerical-drift-analyzer-8yugth           histogram             1   \n14          missing-values-ratio-35881327    count_null_ratio            21   \n15                  unique-ratio-b7b84aee    unique_est_ratio            68   \n16   dull-floralwhite-raven-5521-analyzer               count             2   \n\n    segment_count  anomaly_count  max_anomaly_per_column  \\\n0               1             38                      30   \n1               1             38                      30   \n2               1             38                      30   \n3               1             38                      30   \n4               1             38                      30   \n5               1             38                      30   \n6               1             38                      30   \n7               1             38                      30   \n8               1             81                      25   \n9               1             22                      19   \n10              1             18                      18   \n11              1              8                       8   \n12              1              8                       8   \n13              1              8                       8   \n14              1             30                       4   \n15              1            103                       3   \n16              1              2                       1   \n\n    min_anomaly_per_column  avg_anomaly_per_column  action_count  \\\n0                        2                      12             0   \n1                        2                      12             0   \n2                        2                      12             0   \n3                        2                      12             2   \n4                        2                      12             1   \n5                        2                      12             0   \n6                        2                      12             1   \n7                        2                      12             1   \n8                        2                      10             0   \n9                        1                       7             1   \n10                      18                      18             2   \n11                       8                       8             0   \n12                       8                       8             0   \n13                       8                       8             0   \n14                       1                       1             0   \n15                       1                       1             0   \n16                       1                       1             0   \n\n    action_targets  \n0               []  \n1               []  \n2               []  \n3   [email, slack]  \n4          [email]  \n5               []  \n6          [email]  \n7          [email]  \n8               []  \n9          [email]  \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-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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>3</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>4</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>5</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>6</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>7</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>elated-palegreen-jaguar-6432</td>\n      <td>elated-palegreen-jaguar-6432-analyzer</td>\n      <td>histogram</td>\n      <td>8</td>\n      <td>1</td>\n      <td>81</td>\n      <td>25</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>9</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>22</td>\n      <td>19</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>[email]</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>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>11</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>12</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>13</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>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>14</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>30</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>15</th>\n      <td>unique-ratio-b7b84aee-monitor</td>\n      <td>unique-ratio-b7b84aee</td>\n      <td>unique_est_ratio</td>\n      <td>68</td>\n      <td>1</td>\n      <td>103</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>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>2</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": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -678,11 +672,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:45.451258Z",
-     "start_time": "2024-05-03T20:44:40.012742Z"
+     "end_time": "2024-05-14T19:55:58.356198Z",
+     "start_time": "2024-05-14T19:55:53.450765Z"
     }
    },
    "outputs": [
@@ -691,7 +685,7 @@
      "output_type": "stream",
      "text": [
       "discrete-distribution-22ef37c9-monitor\n",
-      "Diagnosis is for monitor \"discrete-distribution-22ef37c9-monitor\" [discrete-distribution-22ef37c9-monitor] in model-0 org-0, over interval 2024-04-02T00:00:00.000Z/2024-05-02T00:00:00.000Z.\n",
+      "Diagnosis is for monitor \"discrete-distribution-22ef37c9-monitor\" [discrete-distribution-22ef37c9-monitor] in model-0 org-0, over interval 2024-04-13T00:00:00.000Z/2024-05-13T00:00:00.000Z.\n",
       "\n",
       "Analyzer is drift configuration for frequent_items metric with TrailingWindow baseline.\n",
       "Analyzer \"discrete-distribution-22ef37c9\" targets 30 columns and ran on 26 columns in the diagnosed segment.\n",
@@ -700,17 +694,17 @@
       "Diagnostic segment is \"overall\".\n",
       "Diagnostic interval contains 30 batches.\n",
       "\n",
-      "Diagnostic interval rollup contains 1674392 rows for the diagnosed columns.\n",
+      "Diagnostic interval rollup contains 1517489 rows for the diagnosed columns.\n",
       "\n",
       "Analysis results summary:\n",
       "Found non-failed results for 26 columns and 30 batches.\n",
-      "Found 34 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 36.7% (11.0) on average.\n",
+      "Found 38 anomalies in 3 columns, with up to 100.0% (30) batches having anomalies per column and 40.0% (12.0) on average.\n",
       "Columns with anomalies are:\n",
-      "|    | 0               |\n",
-      "|---:|:----------------|\n",
-      "|  0 | ('issue_d', 30) |\n",
-      "|  1 | ('url', 3)      |\n",
-      "|  2 | ('desc', 1)     |\n",
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
       "\n",
       "No failures were detected.\n",
       "\n",
@@ -722,12 +716,12 @@
       "\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",
-      "| issue_d |  30 |\n",
-      "| url     |   3 |\n",
-      "| desc    |   1 |\n",
-      "Accounting for 34 anomalies out of 34\n"
+      "|    | column   |   count |\n",
+      "|---:|:---------|--------:|\n",
+      "|  0 | issue_d  |      30 |\n",
+      "|  1 | url      |       6 |\n",
+      "|  2 | desc     |       2 |\n",
+      "Accounting for 38 anomalies out of 38\n"
      ]
     }
    ],
@@ -749,21 +743,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-03T20:44:45.461470Z",
-     "start_time": "2024-05-03T20:44:45.452512Z"
+     "end_time": "2024-05-14T19:55:58.365346Z",
+     "start_time": "2024-05-14T19:55:58.357308Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "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>"
+      "text/plain": "                            monitor_id                           analyzer_id  \\\n0  frequent-items-drift-monitor-mat0jo  frequent-items-drift-analyzer-mat0jo   \n1  frequent-items-drift-monitor-01rbfl  frequent-items-drift-analyzer-01rbfl   \n2  frequent-items-drift-monitor-3c0hc2  frequent-items-drift-analyzer-3c0hc2   \n3  frequent-items-drift-monitor-9gmtix  frequent-items-drift-analyzer-9gmtix   \n4  frequent-items-drift-monitor-x2hr9z  frequent-items-drift-analyzer-x2hr9z   \n5  inferred-data-type-fec5a735-monitor           inferred-data-type-fec5a735   \n\n               metric  column_count  segment_count  anomaly_count  \\\n0      frequent_items             3              1             38   \n1      frequent_items             3              1             38   \n2      frequent_items             3              1             38   \n3      frequent_items             3              1             38   \n4      frequent_items             3              1             22   \n5  inferred_data_type             1              1             18   \n\n   max_anomaly_per_column  min_anomaly_per_column  avg_anomaly_per_column  \\\n0                      30                       2                      12   \n1                      30                       2                      12   \n2                      30                       2                      12   \n3                      30                       2                      12   \n4                      19                       1                       7   \n5                      18                      18                      18   \n\n   action_count  action_targets  \n0             2  [email, slack]  \n1             1         [email]  \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-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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n    <tr>\n      <th>1</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>2</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>3</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>38</td>\n      <td>30</td>\n      <td>2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>[email]</td>\n    </tr>\n    <tr>\n      <th>4</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>22</td>\n      <td>19</td>\n      <td>1</td>\n      <td>7</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>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>2</td>\n      <td>[email, slack]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,7 +783,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
index 20a6d24..5b7be55 100644
--- a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
+++ b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
@@ -95,7 +95,7 @@ class FailureRecord(BaseModel):
     byTypeCount: List[NamedCount]
 
     def describe(self) -> str:
-        failures = pd.DataFrame([c.to_tuple() for c in self.byColumnCount])
+        failures = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=['column', 'count'])
         failure_types = [t.name for t in self.byTypeCount]
         if len(failures) == 0:
             return "No failures were detected."
@@ -116,7 +116,7 @@ class AnomalyRecord(BaseModel):
     byColumnBatchCount: List[NamedCount]
 
     def describe(self) -> str:
-        counts = pd.Series([c.to_tuple() for c in self.byColumnCount])
+        counts = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=['column', 'count'])
         max_count = int(self.maxAnomalyCount)
         max_pct = max_count * 100 / self.batchCount
         mean_count = float(self.meanAnomalyCount)
@@ -208,8 +208,9 @@ def describe_conditions(self) -> str:
             idx, values = zip(*count_tuples)
             count_by_col = pd.Series(values, idx)
             cols_with_count = filter_by_index(cols.tolist(), count_by_col).sort_values(ascending=False)
-            cols_with_count.rename("anomalies")
-            text += describe_truncated_table(cols_with_count)
+            cols_with_count.index.name = 'column'
+            cols_with_count.name = 'count'
+            text += describe_truncated_table(pd.DataFrame(cols_with_count).reset_index())
             text += f"\nAccounting for {cols_with_count.sum()} anomalies out of " f"{count_by_col.sum()}\n"
 
         return text

From d5c5b8dfb189d0b3973a26d73aa8df32873c9a74 Mon Sep 17 00:00:00 2001
From: Christine Draper <christine@whylabs.ai>
Date: Tue, 14 May 2024 16:21:46 -0400
Subject: [PATCH 2/2] Lint fixes

---
 .../monitor/diagnoser/models/diagnosis_report.py          | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
index 5b7be55..2042bb2 100644
--- a/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
+++ b/whylabs_toolkit/monitor/diagnoser/models/diagnosis_report.py
@@ -95,7 +95,7 @@ class FailureRecord(BaseModel):
     byTypeCount: List[NamedCount]
 
     def describe(self) -> str:
-        failures = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=['column', 'count'])
+        failures = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=["column", "count"])
         failure_types = [t.name for t in self.byTypeCount]
         if len(failures) == 0:
             return "No failures were detected."
@@ -116,7 +116,7 @@ class AnomalyRecord(BaseModel):
     byColumnBatchCount: List[NamedCount]
 
     def describe(self) -> str:
-        counts = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=['column', 'count'])
+        counts = pd.DataFrame([c.to_tuple() for c in self.byColumnCount], columns=["column", "count"])
         max_count = int(self.maxAnomalyCount)
         max_pct = max_count * 100 / self.batchCount
         mean_count = float(self.meanAnomalyCount)
@@ -208,8 +208,8 @@ def describe_conditions(self) -> str:
             idx, values = zip(*count_tuples)
             count_by_col = pd.Series(values, idx)
             cols_with_count = filter_by_index(cols.tolist(), count_by_col).sort_values(ascending=False)
-            cols_with_count.index.name = 'column'
-            cols_with_count.name = 'count'
+            cols_with_count.index.name = "column"
+            cols_with_count.name = "count"
             text += describe_truncated_table(pd.DataFrame(cols_with_count).reset_index())
             text += f"\nAccounting for {cols_with_count.sum()} anomalies out of " f"{count_by_col.sum()}\n"