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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Patch MLflow\n",
+    "Patch MLflow with `whylogs.enable_mlflow()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import whylogs\n",
+    "whylogs.enable_mlflow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import MLFlow\n",
+    "\n",
+    "Set tracking URI to local host.\n",
+    "\n",
+    "We already have an MLflow server running at `http://localhost:5000`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.13.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mlflow\n",
+    "print(mlflow.__version__)\n",
+    "mlflow.set_tracking_uri(\"http://localhost:5000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build a model\n",
+    "\n",
+    "We build a simple ElasticNet model using the wine quality model.\n",
+    "\n",
+    "This is adopted from the MLflow example at: https://mlflow.org/docs/latest/tutorials-and-examples/tutorial.html\n",
+    "\n",
+    "First we need to load the data and split it into train and test datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import sys\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import ElasticNet\n",
+    "from urllib.parse import urlparse\n",
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "\n",
+    "import logging\n",
+    "\n",
+    "logging.basicConfig(level=logging.WARN)\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "def eval_metrics(actual, pred):\n",
+    "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
+    "    mae = mean_absolute_error(actual, pred)\n",
+    "    r2 = r2_score(actual, pred)\n",
+    "    return rmse, mae, r2\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "np.random.seed(40)\n",
+    "# Read the wine-quality csv file from the URL\n",
+    "csv_url = (\n",
+    "    \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+    ")\n",
+    "try:\n",
+    "    data = pd.read_csv(csv_url, sep=\";\")\n",
+    "except Exception as e:\n",
+    "    logger.exception(\n",
+    "        \"Unable to download training & test CSV, check your internet connection. Error: %s\", e\n",
+    "    )\n",
+    "# Split the data into training and test sets. (0.75, 0.25) split.\n",
+    "train, test = train_test_split(data)\n",
+    "# The predicted column is \"quality\" which is a scalar from [3, 9]\n",
+    "train_x = train.drop([\"quality\"], axis=1)\n",
+    "test_x = test.drop([\"quality\"], axis=1)\n",
+    "train_y = train[[\"quality\"]]\n",
+    "test_y = test[[\"quality\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train the model\n",
+    "\n",
+    "We train the model and register them with MLflow.\n",
+    "\n",
+    "Since we already enable whylogs integration, whylogs will pick up the `.whylogs.yaml` in the current directory and add it as a model artifact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# .whylogs.yaml\n",
+      "\n",
+      "# Example WhyLogs YAML configuration\n",
+      "project: demo-project\n",
+      "pipeline: sagemaker-pipeline\n",
+      "verbose: false\n",
+      "writers:\n",
+      "# Save out the full protobuf datasketches data locally\n",
+      "- formats:\n",
+      "    - protobuf\n",
+      "  output_path: s3://whylabs-demo-artifacts-us-west-2/sagemaker\n",
+      "  path_template: $name/dataset_profile\n",
+      "  filename_template: datase_profile-$dataset_timestamp\n",
+      "  type: s3\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat .whylogs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elasticnet model (alpha=0.500000, l1_ratio=0.500000):\n",
+      "  RMSE: 0.7931640229276851\n",
+      "  MAE: 0.6271946374319586\n",
+      "  R2: 0.10862644997792614\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Registered model 'ElasticnetWineModel' already exists. Creating a new version of this model...\n",
+      "2021/01/21 21:37:05 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation.                     Model name: ElasticnetWineModel, version 8\n",
+      "Created version '8' of model 'ElasticnetWineModel'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha = 0.5\n",
+    "l1_ratio = 0.5\n",
+    "with mlflow.start_run():\n",
+    "    lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n",
+    "    lr.fit(train_x, train_y)\n",
+    "    predicted_qualities = lr.predict(test_x)\n",
+    "    (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)\n",
+    "\n",
+    "    print(\"Elasticnet model (alpha=%f, l1_ratio=%f):\" % (alpha, l1_ratio))\n",
+    "    print(\"  RMSE: %s\" % rmse)\n",
+    "    print(\"  MAE: %s\" % mae)\n",
+    "    print(\"  R2: %s\" % r2)\n",
+    "    mlflow.log_param(\"alpha\", alpha)\n",
+    "    mlflow.log_param(\"l1_ratio\", l1_ratio)\n",
+    "    mlflow.log_metric(\"rmse\", rmse)\n",
+    "    mlflow.log_metric(\"r2\", r2)\n",
+    "    mlflow.log_metric(\"mae\", mae)\n",
+    "    tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n",
+    "    # Model registry does not work with file store\n",
+    "    if tracking_url_type_store != \"file\":\n",
+    "        # Register the model\n",
+    "        # There are other ways to use the Model Registry, which depends on the use case,\n",
+    "        # please refer to the doc for more information:\n",
+    "        # https://mlflow.org/docs/latest/model-registry.html#api-workflow\n",
+    "        mlflow.sklearn.log_model(lr, \"model\", registered_model_name=\"ElasticnetWineModel\")\n",
+    "    else:\n",
+    "        mlflow.sklearn.log_model(lr, \"model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Examine run artifact\n",
+    "\n",
+    "We can go to MLflow interfact to see the run artifacts or examine the run info using MLflow API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<RunInfo: artifact_uri='data/0/c67968018081464eabf1bece1dde68f3/artifacts', end_time=1611293825505, experiment_id='0', lifecycle_stage='active', run_id='c67968018081464eabf1bece1dde68f3', run_uuid='c67968018081464eabf1bece1dde68f3', start_time=1611293824555, status='FINISHED', user_id='andy'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "latest_run = mlflow.list_run_infos(experiment_id=\"0\")[0]\n",
+    "latest_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model artifacts\n",
+    "You should see that `.whylogs.yaml` is appended with the model artifacts.\n",
+    "\n",
+    "We also add `whylogs` as a dependency in `conda.yaml`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<FileInfo: file_size=378, is_dir=False, path='model/.whylogs.yaml'>,\n",
+       " <FileInfo: file_size=357, is_dir=False, path='model/MLmodel'>,\n",
+       " <FileInfo: file_size=178, is_dir=False, path='model/conda.yaml'>,\n",
+       " <FileInfo: file_size=645, is_dir=False, path='model/model.pkl'>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = mlflow.tracking.MlflowClient()\n",
+    "info = client.list_artifacts(run_id=latest_run.run_id, path=\"model\")\n",
+    "info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hosting in SageMaker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Push the base container\n",
+    "\n",
+    "Before you start, you'll need to push the base MLflow container to your AWS account. \n",
+    "See https://www.mlflow.org/docs/latest/cli.html#mlflow-sagemaker-build-and-push-container"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!mlflow sagemaker build-and-push-container"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy the model\n",
+    "\n",
+    "MLflow makes it really easy to deploy the model.\n",
+    "\n",
+    "We have setup a Sagemaker execution role that has the S3 write permission to the `whylabs-demo-artifacts-us-west-2` bucket (set in `.whylogs.yaml`)\n",
+    "\n",
+    "Note that it takes a while for a model deployment to reach Stable state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021/01/21 21:50:59 INFO mlflow.sagemaker: Using the python_function flavor for deployment!\n",
+      "2021/01/21 21:50:59 INFO mlflow.sagemaker: No model data bucket specified, using the default bucket\n",
+      "2021/01/21 21:51:01 INFO mlflow.sagemaker: Default bucket `mlflow-sagemaker-us-west-2-207285235248` already exists. Skipping creation.\n",
+      "2021/01/21 21:51:01 INFO mlflow.sagemaker: tag response: {'ResponseMetadata': {'RequestId': '55E816D19DDB953D', 'HostId': 't7zVriBj3Gdtwn7nXaIMGWfsLBpZqTnzBWF+oEjdnAP7g79bEgJ9XtTwFNNAn8AgQDcFkiQGX5Y=', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amz-id-2': 't7zVriBj3Gdtwn7nXaIMGWfsLBpZqTnzBWF+oEjdnAP7g79bEgJ9XtTwFNNAn8AgQDcFkiQGX5Y=', 'x-amz-request-id': '55E816D19DDB953D', 'date': 'Fri, 22 Jan 2021 05:51:02 GMT', 'content-length': '0', 'server': 'AmazonS3'}, 'RetryAttempts': 0}}\n",
+      "2021/01/21 21:51:01 INFO mlflow.sagemaker: Creating new endpoint with name: whylogs ...\n",
+      "2021/01/21 21:51:02 INFO mlflow.sagemaker: Created model with arn: arn:aws:sagemaker:us-west-2:207285235248:model/whylogs-model-lesbbs-gqzcqas6ylo-bcg\n",
+      "2021/01/21 21:51:02 INFO mlflow.sagemaker: Created endpoint configuration with arn: arn:aws:sagemaker:us-west-2:207285235248:endpoint-config/whylogs-config-xn7ixhfgt46h-ucr1xpnabq\n",
+      "2021/01/21 21:51:02 INFO mlflow.sagemaker: Created endpoint with arn: arn:aws:sagemaker:us-west-2:207285235248:endpoint/whylogs\n",
+      "2021/01/21 21:51:02 INFO mlflow.sagemaker: Waiting for the deployment operation to complete...\n",
+      "2021/01/21 21:51:02 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:51:22 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:51:43 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:52:03 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:52:23 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:52:43 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:53:04 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:53:24 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:53:44 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:54:04 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:54:25 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:54:45 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:55:05 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:55:26 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:55:46 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:56:06 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:56:26 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:56:46 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:57:07 INFO mlflow.sagemaker: Waiting for endpoint to reach the \"InService\" state. Current endpoint status: \"Creating\"\n",
+      "2021/01/21 21:57:17 INFO mlflow.sagemaker: The deployment operation completed successfully with message: \"The SageMaker endpoint was created successfully.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mlflow.sagemaker\n",
+    "\n",
+    "mlflow.sagemaker.deploy(\n",
+    "    app_name=\"whylogs\", \n",
+    "    model_uri=f\"runs:/{latest_run.run_id}/model\", \n",
+    "    execution_role_arn=\"arn:aws:iam::207285235248:role/service-role/AmazonSageMaker-ExecutionRole-20200529T103706\",\n",
+    "    mode=\"replace\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sagemaker API Input\n",
+    "\n",
+    "The input of SageMaker API is based on Pandas JSON wplit `split` orient. Example is below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'{\"columns\":[\"fixed acidity\",\"volatile acidity\",\"citric acid\",\"residual sugar\",\"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\"pH\",\"sulphates\",\"alcohol\"],\"index\":[1316,1507,849],\"data\":[[5.4,0.74,0.0,1.2,0.041,16.0,46.0,0.99258,4.01,0.59,12.5],[7.5,0.38,0.57,2.3,0.106,5.0,12.0,0.99605,3.36,0.55,11.4],[6.4,0.63,0.21,1.6,0.08,12.0,32.0,0.99689,3.58,0.66,9.8]]}'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_x.head(3).to_json(orient='split')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calling the endpoint\n",
+    "\n",
+    "First we create a Sagemaker client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "client = boto3.client('runtime.sagemaker')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call the endpoint and see the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5.731344540042413, 5.247960704489776, 5.754717372954283, 5.751114228483034, 5.635968698470214, 5.650958816069663, 5.6375782441532145, 5.781663284991655, 5.578837218114763, 5.603618905770178]\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = client.invoke_endpoint(EndpointName=\"hackathon\", ContentType=\"application/json\", Body=test_x.head(10).to_json(orient=\"split\"))\n",
+    "print(res['Body'].read().decode())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run our data for a while\n",
+    "To demonstrate the ability to run whylogs in a live inference environment, we'll run the above request a few time.\n",
+    "\n",
+    "By default, whylogs writes to S3 every minutes (configurable). The short interval is for demo and testing purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "for i in range(1, 30):\n",
+    "    res = client.invoke_endpoint(EndpointName=\"whylogs\", ContentType=\"application/json\", Body=test_x.to_json(orient=\"split\"))\n",
+    "    res['Body'].read().decode()\n",
+    "    time.sleep(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Verify the data in the bucket\n",
+    "\n",
+    "We verify that whylogs sends data in a period manner to the bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-01-21 22:16:27      38653 sagemaker/live/dataset_profile/protobuf/datase_profile-1611296040000.2021-01-22_06-14.bin\n",
+      "2021-01-21 22:17:57      41127 sagemaker/live/dataset_profile/protobuf/datase_profile-1611296100000.2021-01-22_06-15.bin\n",
+      "2021-01-21 22:18:29      38653 sagemaker/live/dataset_profile/protobuf/datase_profile-1611296160000.2021-01-22_06-16.bin\n",
+      "2021-01-21 22:18:50      45135 sagemaker/live/dataset_profile/protobuf/datase_profile-1611296220000.2021-01-22_06-17.bin\n"
+     ]
+    }
+   ],
+   "source": [
+    "!aws s3 ls --recursive s3://whylabs-demo-artifacts-us-west-2/sagemaker/live/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should be able to analyze this data using `whylogs` visualization. Check out our example repo for more notebooks!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hackathon",
+   "language": "python",
+   "name": "hackathon"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/whylogs_pyspark_example.ipynb b/python/whylogs_pyspark_example.ipynb
new file mode 100644
index 0000000..220eb14
--- /dev/null
+++ b/python/whylogs_pyspark_example.ipynb
@@ -0,0 +1,23224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# whylogs + pySpark\n",
+    "\n",
+    "First, you'll need to point to an existing Spark installation. Make sure that the Spark version matches the pyspark version in the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: SPARK_HOME=/Users/andy/Workspace/spark/spark-3.0.1-bin-hadoop2.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "env SPARK_HOME=/Users/andy/Workspace/spark/spark-3.0.1-bin-hadoop2.7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## whylogs Spark jar\n",
+    "\n",
+    "You'll need to load a fat jar for whylogs. This jar contains all the required dependencies (some are shaded) for running whylogs in Spark.\n",
+    "\n",
+    "Spark will then inject the `whyspark` module into the path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "whylogs_jar = \"/Volumes/Workspace/whylogs-java/spark-bundle/build/libs/whylogs-spark-bundle_2.12-0.1.2-b0.jar\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyspark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/andy/miniconda3/envs/whylogs-spark/bin/python'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.executable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark = pyspark.sql.SparkSession.builder \\\n",
+    "                .appName(\"whylogs\") \\\n",
+    "                .config(\"spark.pyspark.python\", sys.executable) \\\n",
+    "                .config(\"spark.executor.userClassPathFirst\", \"true\") \\\n",
+    "                .config(\"spark.submit.pyFiles\", whylogs_jar) \\\n",
+    "                .config(\"spark.jars\", whylogs_jar) \\\n",
+    "                .getOrCreate() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "                <p><b>SparkSession - in-memory</b></p>\n",
+       "                \n",
+       "        <div>\n",
+       "            <p><b>SparkContext</b></p>\n",
+       "\n",
+       "            <p><a href=\"http://192.168.86.38:4040\">Spark UI</a></p>\n",
+       "\n",
+       "            <dl>\n",
+       "              <dt>Version</dt>\n",
+       "                <dd><code>v3.0.1</code></dd>\n",
+       "              <dt>Master</dt>\n",
+       "                <dd><code>local[*]</code></dd>\n",
+       "              <dt>AppName</dt>\n",
+       "                <dd><code>whylogs</code></dd>\n",
+       "            </dl>\n",
+       "        </div>\n",
+       "        \n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<pyspark.sql.session.SparkSession at 0x7fd3486e4cd0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Spark bridge\n",
+    "\n",
+    "`whyspark` is the module that is bundled in the above jar. It's a thin bridge into whylogs Spark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from whyspark import new_profiling_session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = spark.read.csv(\"full-data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "profile_df = new_profiling_session(df, \"demo\").aggProfiles()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## whylogs data\n",
+    "\n",
+    "whylogs output is marked as \"BinaryType\" and thus cannot be viewed in Spark. You'll need to use actual whylogs library in Python to handle this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+\n",
+      "|         why_profile|\n",
+      "+--------------------+\n",
+      "|[E4 DF 32 0A 2A 0...|\n",
+      "+--------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "profile_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Save to Parquet\n",
+    "profile_df.write.mode('overwrite').parquet(\"whylogs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pdf = profile_df.toPandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>why_profile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[228, 223, 50, 10, 42, 8, 1, 16, 1, 26, 4, 100...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         why_profile\n",
+       "0  [228, 223, 50, 10, 42, 8, 1, 16, 1, 26, 4, 100..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import whylogs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2.0-dev0\n"
+     ]
+    }
+   ],
+   "source": [
+    "assert whylogs.__version__ >= '0.2.0'\n",
+    "print(whylogs.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from whylogs import DatasetProfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pdf['profile'] = pdf['why_profile'].apply(DatasetProfile.parse_delimited)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "properties {\n",
+       "  schema_major_version: 1\n",
+       "  schema_minor_version: 1\n",
+       "  session_id: \"demo\"\n",
+       "  session_timestamp: 1612846518762\n",
+       "  data_timestamp: -1\n",
+       "  tags {\n",
+       "    key: \"Name\"\n",
+       "    value: \"demo\"\n",
+       "  }\n",
+       "  tags {\n",
+       "    key: \"name\"\n",
+       "    value: \"\"\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c0\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
+       "      count: 98679\n",
+       "      min: 1000.0\n",
+       "      max: 40000.0\n",
+       "      mean: 15030.950100831991\n",
+       "      stddev: 8756.094826211576\n",
+       "      histogram {\n",
+       "        start: 1000.0\n",
+       "        end: 40000.004\n",
+       "        counts: 2038\n",
+       "        counts: 3392\n",
+       "        counts: 2688\n",
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+       "        counts: 4992\n",
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+       "        counts: 9664\n",
+       "        counts: 3200\n",
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+       "        counts: 3584\n",
+       "        counts: 6272\n",
+       "        counts: 4288\n",
+       "        counts: 2176\n",
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+       "        counts: 1984\n",
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+       "        counts: 1024\n",
+       "        counts: 64\n",
+       "        counts: 256\n",
+       "        counts: 3328\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 768\n",
+       "        max: 40000.0\n",
+       "        min: 1000.0\n",
+       "        bins: 1000.0\n",
+       "        bins: 2300.0001333333335\n",
+       "        bins: 3600.000266666667\n",
+       "        bins: 4900.000400000001\n",
+       "        bins: 6200.000533333334\n",
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+       "        bins: 8800.000800000002\n",
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+       "        bins: 38700.00386666667\n",
+       "        bins: 40000.004\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 391.0\n",
+       "        upper: 391.0\n",
+       "        lower: 391.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 1000.0\n",
+       "        quantile_values: 1500.0\n",
+       "        quantile_values: 3600.0\n",
+       "        quantile_values: 8000.0\n",
+       "        quantile_values: 13800.0\n",
+       "        quantile_values: 20000.0\n",
+       "        quantile_values: 32000.0\n",
+       "        quantile_values: 35000.0\n",
+       "        quantile_values: 40000.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 3\n",
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+       "        }\n",
+       "        doubles {\n",
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+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 6218\n",
+       "        json_value: \"10000.000000000000000\"\n",
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+       "        estimate: 2192\n",
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+       "      }\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 1748\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=7000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1748\n",
+       "        json_value: \"7000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1550\n",
+       "        json_value: \"14000.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c1\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
+       "      count: 98679\n",
+       "      min: 1000.0\n",
+       "      max: 40000.0\n",
+       "      mean: 15030.950100831991\n",
+       "      stddev: 8756.094826211576\n",
+       "      histogram {\n",
+       "        start: 1000.0\n",
+       "        end: 40000.004\n",
+       "        counts: 1846\n",
+       "        counts: 3392\n",
+       "        counts: 2688\n",
+       "        counts: 9536\n",
+       "        counts: 4736\n",
+       "        counts: 5632\n",
+       "        counts: 9920\n",
+       "        counts: 3008\n",
+       "        counts: 6976\n",
+       "        counts: 3328\n",
+       "        counts: 6272\n",
+       "        counts: 4416\n",
+       "        counts: 2176\n",
+       "        counts: 4032\n",
+       "        counts: 7040\n",
+       "        counts: 2432\n",
+       "        counts: 1600\n",
+       "        counts: 4545\n",
+       "        counts: 2752\n",
+       "        counts: 960\n",
+       "        counts: 1984\n",
+       "        counts: 704\n",
+       "        counts: 3072\n",
+       "        counts: 960\n",
+       "        counts: 384\n",
+       "        counts: 256\n",
+       "        counts: 3072\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 960\n",
+       "        max: 40000.0\n",
+       "        min: 1000.0\n",
+       "        bins: 1000.0\n",
+       "        bins: 2300.0001333333335\n",
+       "        bins: 3600.000266666667\n",
+       "        bins: 4900.000400000001\n",
+       "        bins: 6200.000533333334\n",
+       "        bins: 7500.000666666667\n",
+       "        bins: 8800.000800000002\n",
+       "        bins: 10100.000933333335\n",
+       "        bins: 11400.001066666668\n",
+       "        bins: 12700.0012\n",
+       "        bins: 14000.001333333334\n",
+       "        bins: 15300.001466666668\n",
+       "        bins: 16600.001600000003\n",
+       "        bins: 17900.001733333334\n",
+       "        bins: 19200.00186666667\n",
+       "        bins: 20500.002\n",
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+       "        bins: 28300.002800000002\n",
+       "        bins: 29600.002933333337\n",
+       "        bins: 30900.003066666668\n",
+       "        bins: 32200.003200000003\n",
+       "        bins: 33500.00333333334\n",
+       "        bins: 34800.00346666667\n",
+       "        bins: 36100.003600000004\n",
+       "        bins: 37400.00373333334\n",
+       "        bins: 38700.00386666667\n",
+       "        bins: 40000.004\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 391.0\n",
+       "        upper: 391.0\n",
+       "        lower: 391.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 1000.0\n",
+       "        quantile_values: 2000.0\n",
+       "        quantile_values: 3600.0\n",
+       "        quantile_values: 8000.0\n",
+       "        quantile_values: 13825.0\n",
+       "        quantile_values: 20000.0\n",
+       "        quantile_values: 32000.0\n",
+       "        quantile_values: 36000.0\n",
+       "        quantile_values: 40000.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 3\n",
+       "          value: 6000.0\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 30000.0\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 5000.0\n",
+       "          rank: 2\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 14400.0\n",
+       "          rank: 3\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 22000.0\n",
+       "          rank: 4\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 14775.0\n",
+       "          rank: 5\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 20000.0\n",
+       "          rank: 6\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 35000.0\n",
+       "          rank: 7\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 20600.0\n",
+       "          rank: 8\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 2500.0\n",
+       "          rank: 9\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 6218\n",
+       "        json_value: \"10000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 6218\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=10000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5972\n",
+       "        json_value: \"20000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5972\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5324\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=12000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5324\n",
+       "        json_value: \"12000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4478\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4478\n",
+       "        json_value: \"15000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3764\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3764\n",
+       "        json_value: \"24000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3644\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=6000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3644\n",
+       "        json_value: \"6000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3461\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=8000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3461\n",
+       "        json_value: \"8000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3446\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3445\n",
+       "        json_value: \"5000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3032\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=16000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3032\n",
+       "        json_value: \"16000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2994\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=35000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2994\n",
+       "        json_value: \"35000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2729\n",
+       "        json_value: \"30000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2729\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=30000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2192\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=18000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2190\n",
+       "        json_value: \"18000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2171\n",
+       "        json_value: \"25000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2171\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=25000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1748\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=7000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1748\n",
+       "        json_value: \"7000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1550\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1550\n",
+       "        json_value: \"14000.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c10\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 49070\n",
+       "        json_value: \"MORTGAGE\"\n",
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+       "      items {\n",
+       "        estimate: 38430\n",
+       "        json_value: \"RENT\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 11179\n",
+       "        json_value: \"OWN\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"home_ownership\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c100\"\n",
+       "  value {\n",
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+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
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+       "      type_counts {\n",
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+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 98647\n",
+       "        json_value: \"N\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 32\n",
+       "        json_value: \"Y\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"hardship_flag\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c101\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 97612\n",
+       "        json_value: \"Cash\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1067\n",
+       "        json_value: \"DirectPay\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"disbursement_method\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c102\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 96251\n",
+       "        json_value: \"N\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2428\n",
+       "        json_value: \"Y\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"debt_settlement_flag\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c103\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      min: 0.990352462094009\n",
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+       "      mean: 7.439215517440383\n",
+       "      stddev: 2.0031269614529315\n",
+       "      histogram {\n",
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+       "        counts: 1728\n",
+       "        counts: 2048\n",
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+       "        counts: 2688\n",
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+       "        counts: 1984\n",
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+       "        counts: 3968\n",
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+       "        counts: 6592\n",
+       "        max: 10.544709205627441\n",
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+       "        bins: 0.9903524518013\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 2099.0\n",
+       "        upper: 2099.0\n",
+       "        lower: 2099.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.9903524518013\n",
+       "        quantile_values: 1.9609473943710327\n",
+       "        quantile_values: 3.659207344055176\n",
+       "        quantile_values: 6.3761444091796875\n",
+       "        quantile_values: 7.74592399597168\n",
+       "        quantile_values: 8.973093032836914\n",
+       "        quantile_values: 10.274190902709961\n",
+       "        quantile_values: 10.417323112487793\n",
+       "        quantile_values: 10.544709205627441\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 4.305784644561099\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 5.515814380066589\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 8.705800839173506\n",
+       "          rank: 2\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 6.401511521068653\n",
+       "          rank: 3\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 4.156538247822754\n",
+       "          rank: 4\n",
+       "        }\n",
+       "        doubles {\n",
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+       "        json_value: \"9.248479852539612\"\n",
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+       "      }\n",
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+       "        json_value: \"4.471931778854828\"\n",
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+       "        json_value: \"9.027526373994622\"\n",
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+       "      }\n",
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+       "      }\n",
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+       "        estimate: 2035\n",
+       "        json_value: \"8.455592638967394\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=8.684288644426601, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"8.003324261026901\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c104\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
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+       "    schema {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"mths_since_rcnt_il\"\n",
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+       "columns {\n",
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+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "      }\n",
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+       "    number_summary {\n",
+       "      count: 98679\n",
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+       "      mean: 77172.73708762755\n",
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+       "      histogram {\n",
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+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
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+       "        counts: 0\n",
+       "        max: 600000.0\n",
+       "        min: 11000.0\n",
+       "        bins: 11000.0\n",
+       "        bins: 30633.335333333336\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 472.0\n",
+       "        upper: 472.0\n",
+       "        lower: 472.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 11000.0\n",
+       "        quantile_values: 17280.0\n",
+       "        quantile_values: 26642.80078125\n",
+       "        quantile_values: 45000.0\n",
+       "        quantile_values: 65000.0\n",
+       "        quantile_values: 93600.0\n",
+       "        quantile_values: 167000.0\n",
+       "        quantile_values: 300000.0\n",
+       "        quantile_values: 600000.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 56000.0\n",
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+       "        doubles {\n",
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+       "          value: 205000.0\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "      }\n",
+       "      is_discrete: true\n",
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+       "    frequent_items {\n",
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+       "      items {\n",
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+       "        json_value: \"65000.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=65000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"40000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=40000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2852\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=50000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2852\n",
+       "        json_value: \"50000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=75000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2716\n",
+       "        json_value: \"75000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=80000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2637\n",
+       "        json_value: \"80000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=45000.0, stringValue=null)\"\n",
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+       "      items {\n",
+       "        estimate: 2585\n",
+       "        json_value: \"45000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=55000.0, stringValue=null)\"\n",
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+       "      items {\n",
+       "        estimate: 2566\n",
+       "        json_value: \"55000.000000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 2437\n",
+       "        json_value: \"70000.000000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 2437\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=70000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1930\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=100000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1930\n",
+       "        json_value: \"100000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1894\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=85000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1894\n",
+       "        json_value: \"85000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1873\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=90000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1872\n",
+       "        json_value: \"90000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1867\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=42000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1866\n",
+       "        json_value: \"42000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1862\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=120000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1862\n",
+       "        json_value: \"120000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1860\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=30000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1860\n",
+       "        json_value: \"30000.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c12\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 40056\n",
+       "        json_value: \"Source Verified\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 34855\n",
+       "        json_value: \"Not Verified\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 23768\n",
+       "        json_value: \"Verified\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"verification_status\"\n",
+       "      }\n",
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+       "  }\n",
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+       "columns {\n",
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+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 98679\n",
+       "        json_value: \"2020-12-02\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"issue_d\"\n",
+       "      }\n",
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+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c14\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 61184\n",
+       "        json_value: \"Fully Paid\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 19424\n",
+       "        json_value: \"Current\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 16697\n",
+       "        json_value: \"Charged Off\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1048\n",
+       "        json_value: \"Late (31-120 days)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 180\n",
+       "        json_value: \"In Grace Period\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 146\n",
+       "        json_value: \"Late (16-30 days)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"loan_status\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c15\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 98647\n",
+       "        json_value: \"n\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 32\n",
+       "        json_value: \"y\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"pymnt_plan\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c16\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 1019\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75720060\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75820284\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75321619\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75709203\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73704473\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75923611\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75141600\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75819394\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=70944816\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73609580\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=74815857\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73488636\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75194762\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75322834\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75577859\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=74592646\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73531191\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=74592690\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=72798002\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=72554856\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=72033065\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73691126\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=74824774\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75924006\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=74492878\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75301812\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=73733954\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75061383\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=75858687\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1018\n",
+       "        json_value: \"https://lendingclub.com/browse/loanDetail.action?loan_id=72163280\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c17\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
+       "        value: 98631\n",
+       "      }\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 49\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 48\n",
+       "        json_value: \" \"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"desc\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c18\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 57488\n",
+       "        json_value: \"debt_consolidation\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 21842\n",
+       "        json_value: \"credit_card\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 6699\n",
+       "        json_value: \"home_improvement\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5642\n",
+       "        json_value: \"other\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1955\n",
+       "        json_value: \"major_purchase\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1538\n",
+       "        json_value: \"small_business\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 888\n",
+       "        json_value: \"medical\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 711\n",
+       "        json_value: \"car\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 654\n",
+       "        json_value: \"moving\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 650\n",
+       "        json_value: \"house\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 511\n",
+       "        json_value: \"vacation\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 101\n",
+       "        json_value: \"renewable_energy\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"purpose\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c19\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
+       "        value: 6619\n",
+       "      }\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 92061\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 53483\n",
+       "        json_value: \"Debt consolidation\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 19757\n",
+       "        json_value: \"Credit card refinancing\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 6368\n",
+       "        json_value: \"Home improvement\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5539\n",
+       "        json_value: \"Other\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1909\n",
+       "        json_value: \"Major purchase\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1538\n",
+       "        json_value: \"Business\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 888\n",
+       "        json_value: \"Medical expenses\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 711\n",
+       "        json_value: \"Car financing\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 654\n",
+       "        json_value: \"Moving and relocation\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 601\n",
+       "        json_value: \"Home buying\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 511\n",
+       "        json_value: \"Vacation\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 101\n",
+       "        json_value: \"Green loan\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"title\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c2\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
+       "      count: 98679\n",
+       "      min: 1000.0\n",
+       "      max: 40000.0\n",
+       "      mean: 15019.43929306134\n",
+       "      stddev: 8753.04369850172\n",
+       "      histogram {\n",
+       "        start: 1000.0\n",
+       "        end: 40000.004\n",
+       "        counts: 1910\n",
+       "        counts: 3584\n",
+       "        counts: 2752\n",
+       "        counts: 9280\n",
+       "        counts: 4992\n",
+       "        counts: 5568\n",
+       "        counts: 9984\n",
+       "        counts: 3008\n",
+       "        counts: 6656\n",
+       "        counts: 3648\n",
+       "        counts: 6272\n",
+       "        counts: 4416\n",
+       "        counts: 1984\n",
+       "        counts: 3968\n",
+       "        counts: 7296\n",
+       "        counts: 2560\n",
+       "        counts: 1408\n",
+       "        counts: 4417\n",
+       "        counts: 2624\n",
+       "        counts: 1344\n",
+       "        counts: 1728\n",
+       "        counts: 640\n",
+       "        counts: 3264\n",
+       "        counts: 1024\n",
+       "        counts: 320\n",
+       "        counts: 384\n",
+       "        counts: 2688\n",
+       "        counts: 256\n",
+       "        counts: 0\n",
+       "        counts: 704\n",
+       "        max: 40000.0\n",
+       "        min: 1000.0\n",
+       "        bins: 1000.0\n",
+       "        bins: 2300.0001333333335\n",
+       "        bins: 3600.000266666667\n",
+       "        bins: 4900.000400000001\n",
+       "        bins: 6200.000533333334\n",
+       "        bins: 7500.000666666667\n",
+       "        bins: 8800.000800000002\n",
+       "        bins: 10100.000933333335\n",
+       "        bins: 11400.001066666668\n",
+       "        bins: 12700.0012\n",
+       "        bins: 14000.001333333334\n",
+       "        bins: 15300.001466666668\n",
+       "        bins: 16600.001600000003\n",
+       "        bins: 17900.001733333334\n",
+       "        bins: 19200.00186666667\n",
+       "        bins: 20500.002\n",
+       "        bins: 21800.002133333335\n",
+       "        bins: 23100.00226666667\n",
+       "        bins: 24400.0024\n",
+       "        bins: 25700.002533333336\n",
+       "        bins: 27000.002666666667\n",
+       "        bins: 28300.002800000002\n",
+       "        bins: 29600.002933333337\n",
+       "        bins: 30900.003066666668\n",
+       "        bins: 32200.003200000003\n",
+       "        bins: 33500.00333333334\n",
+       "        bins: 34800.00346666667\n",
+       "        bins: 36100.003600000004\n",
+       "        bins: 37400.00373333334\n",
+       "        bins: 38700.00386666667\n",
+       "        bins: 40000.004\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 445.0\n",
+       "        upper: 445.0\n",
+       "        lower: 445.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 1000.0\n",
+       "        quantile_values: 1500.0\n",
+       "        quantile_values: 3500.0\n",
+       "        quantile_values: 8000.0\n",
+       "        quantile_values: 13550.0\n",
+       "        quantile_values: 20000.0\n",
+       "        quantile_values: 31950.0\n",
+       "        quantile_values: 35000.0\n",
+       "        quantile_values: 40000.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 3\n",
+       "          value: 6000.0\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 30000.0\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 5000.0\n",
+       "          rank: 2\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 14400.0\n",
+       "          rank: 3\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 21900.0\n",
+       "          rank: 4\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 14775.0\n",
+       "          rank: 5\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 20000.0\n",
+       "          rank: 6\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 35000.0\n",
+       "          rank: 7\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 20600.0\n",
+       "          rank: 8\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 2500.0\n",
+       "          rank: 9\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 6012\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=10000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 6012\n",
+       "        json_value: \"10000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5598\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 5598\n",
+       "        json_value: \"20000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4895\n",
+       "        json_value: \"12000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4895\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=12000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4303\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 4303\n",
+       "        json_value: \"15000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3459\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3459\n",
+       "        json_value: \"24000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3452\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=6000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3452\n",
+       "        json_value: \"6000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3308\n",
+       "        json_value: \"8000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3308\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=8000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3219\n",
+       "        json_value: \"5000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 3219\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2779\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=16000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2779\n",
+       "        json_value: \"16000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2638\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=35000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2637\n",
+       "        json_value: \"35000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2588\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=30000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2588\n",
+       "        json_value: \"30000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2151\n",
+       "        json_value: \"18000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=18000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1987\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=25000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1986\n",
+       "        json_value: \"25000.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"7000.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=7000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1519\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14000.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 1518\n",
+       "        json_value: \"14000.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
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+       "  value {\n",
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+       "    }\n",
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+       "      }\n",
+       "      type_counts {\n",
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+       "      }\n",
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+       "        json_value: \"190xx\"\n",
+       "      }\n",
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+       "        json_value: \"463xx\"\n",
+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"132xx\"\n",
+       "      }\n",
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+       "  }\n",
+       "}\n",
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+       "  value {\n",
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+       "    }\n",
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+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"CA\"\n",
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+       "        json_value: \"NY\"\n",
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+       "        json_value: \"TX\"\n",
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+       "        json_value: \"NJ\"\n",
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+       "        json_value: \"NC\"\n",
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+       "        json_value: \"VA\"\n",
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+       "        json_value: \"MD\"\n",
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+       "        json_value: \"WA\"\n",
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+       "        json_value: \"AZ\"\n",
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+       "        json_value: \"TN\"\n",
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+       "        json_value: \"AL\"\n",
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+       "        json_value: \"CT\"\n",
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+       "        json_value: \"IN\"\n",
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+       "        json_value: \"LA\"\n",
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+       "        json_value: \"SC\"\n",
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+       "        json_value: \"OK\"\n",
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+       "        json_value: \"WI\"\n",
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+       "        json_value: \"OR\"\n",
+       "      }\n",
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+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
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+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
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+       "    number_summary {\n",
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+       "      stddev: 9.14099351155337\n",
+       "      histogram {\n",
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+       "        max: 52.720001220703125\n",
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+       "        n: 98679\n",
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+       "      unique_count {\n",
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+       "        lower: 1528.0\n",
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+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 2.130000114440918\n",
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+       "        quantile_values: 12.699999809265137\n",
+       "        quantile_values: 18.440000534057617\n",
+       "        quantile_values: 25.56999969482422\n",
+       "        quantile_values: 35.619998931884766\n",
+       "        quantile_values: 42.630001068115234\n",
+       "        quantile_values: 52.720001220703125\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          value: 10.56\n",
+       "        }\n",
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+       "          rank: 1\n",
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+       "        doubles {\n",
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+       "          rank: 11\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
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+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"14.730000000000000\"\n",
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+       "        n: 98679\n",
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+       "        quantile_values: 4.0\n",
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+       "        json_value: \"delinq_2yrs\"\n",
+       "      }\n",
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+       "        bins: 0.6666667333333333\n",
+       "        bins: 0.8333334166666666\n",
+       "        bins: 1.0000000999999998\n",
+       "        bins: 1.1666667833333333\n",
+       "        bins: 1.3333334666666665\n",
+       "        bins: 1.5000001499999998\n",
+       "        bins: 1.6666668333333332\n",
+       "        bins: 1.8333335166666664\n",
+       "        bins: 2.0000001999999997\n",
+       "        bins: 2.166666883333333\n",
+       "        bins: 2.3333335666666666\n",
+       "        bins: 2.50000025\n",
+       "        bins: 2.666666933333333\n",
+       "        bins: 2.8333336166666663\n",
+       "        bins: 3.0000002999999995\n",
+       "        bins: 3.166666983333333\n",
+       "        bins: 3.3333336666666664\n",
+       "        bins: 3.5000003499999996\n",
+       "        bins: 3.666667033333333\n",
+       "        bins: 3.833333716666666\n",
+       "        bins: 4.000000399999999\n",
+       "        bins: 4.166667083333333\n",
+       "        bins: 4.333333766666666\n",
+       "        bins: 4.50000045\n",
+       "        bins: 4.666667133333333\n",
+       "        bins: 4.833333816666666\n",
+       "        bins: 5.0000005\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 6.0\n",
+       "        upper: 6.0\n",
+       "        lower: 6.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 1.0\n",
+       "        quantile_values: 2.0\n",
+       "        quantile_values: 4.0\n",
+       "        quantile_values: 5.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 6\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 5\n",
+       "          value: 1.0\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 2.0\n",
+       "          rank: 2\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 60743\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=0.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 60743\n",
+       "        json_value: \"0.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 26284\n",
+       "        json_value: \"1.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 26284\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=1.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 7990\n",
+       "        json_value: \"2.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 7990\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=2.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2444\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=3.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2444\n",
+       "        json_value: \"3.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 960\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=4.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 960\n",
+       "        json_value: \"4.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 258\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 258\n",
+       "        json_value: \"5.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"inq_last_6mths\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c28\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
+       "        value: 49893\n",
+       "      }\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999795027363847\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 48786\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
+       "      count: 48786\n",
+       "      max: 100.0\n",
+       "      mean: 33.89697864141352\n",
+       "      stddev: 22.09118422609941\n",
+       "      histogram {\n",
+       "        end: 100.00001\n",
+       "        counts: 1554\n",
+       "        counts: 2640\n",
+       "        counts: 3376\n",
+       "        counts: 2992\n",
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+       "        counts: 1824\n",
+       "        counts: 2000\n",
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+       "        counts: 2064\n",
+       "        counts: 1936\n",
+       "        counts: 1536\n",
+       "        counts: 1216\n",
+       "        counts: 1200\n",
+       "        counts: 1328\n",
+       "        counts: 1488\n",
+       "        counts: 1072\n",
+       "        counts: 1376\n",
+       "        counts: 864\n",
+       "        counts: 1376\n",
+       "        counts: 1360\n",
+       "        counts: 176\n",
+       "        counts: 128\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 128\n",
+       "        max: 100.0\n",
+       "        bins: 0.0\n",
+       "        bins: 3.333333666666667\n",
+       "        bins: 6.666667333333334\n",
+       "        bins: 10.000001000000001\n",
+       "        bins: 13.333334666666667\n",
+       "        bins: 16.666668333333334\n",
+       "        bins: 20.000002000000002\n",
+       "        bins: 23.333335666666667\n",
+       "        bins: 26.666669333333335\n",
+       "        bins: 30.000003000000003\n",
+       "        bins: 33.33333666666667\n",
+       "        bins: 36.666670333333336\n",
+       "        bins: 40.000004000000004\n",
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+       "        bins: 60.000006000000006\n",
+       "        bins: 63.33333966666667\n",
+       "        bins: 66.66667333333334\n",
+       "        bins: 70.00000700000001\n",
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+       "        bins: 80.00000800000001\n",
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+       "        bins: 86.66667533333334\n",
+       "        bins: 90.000009\n",
+       "        bins: 93.33334266666667\n",
+       "        bins: 96.66667633333334\n",
+       "        bins: 100.00001\n",
+       "        n: 48786\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 87.0\n",
+       "        upper: 87.0\n",
+       "        lower: 87.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 1.0\n",
+       "        quantile_values: 4.0\n",
+       "        quantile_values: 15.0\n",
+       "        quantile_values: 30.0\n",
+       "        quantile_values: 49.0\n",
+       "        quantile_values: 75.0\n",
+       "        quantile_values: 80.0\n",
+       "        quantile_values: 100.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 16.0\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 74.0\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 22.0\n",
+       "          rank: 2\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 45.0\n",
+       "          rank: 3\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 6.0\n",
+       "          rank: 4\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 24.0\n",
+       "          rank: 5\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 18.0\n",
+       "          rank: 6\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 61.0\n",
+       "          rank: 7\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 5.0\n",
+       "          rank: 8\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 21.0\n",
+       "          rank: 9\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 13.0\n",
+       "          rank: 10\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 29.0\n",
+       "          rank: 11\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 1397\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1397\n",
+       "        json_value: \"15.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1368\n",
+       "        json_value: \"12.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1368\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=12.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1237\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1237\n",
+       "        json_value: \"20.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1230\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=13.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1230\n",
+       "        json_value: \"13.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1198\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=40.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1196\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=36.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1196\n",
+       "        json_value: \"40.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1196\n",
+       "        json_value: \"36.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1195\n",
+       "        json_value: \"17.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1195\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=17.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1193\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=7.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1192\n",
+       "        json_value: \"6.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1192\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=6.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1190\n",
+       "        json_value: \"7.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1187\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=39.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1186\n",
+       "        json_value: \"39.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1186\n",
+       "        json_value: \"44.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1185\n",
+       "        json_value: \"26.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1185\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=26.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1184\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=44.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1184\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=4.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1184\n",
+       "        json_value: \"22.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1183\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1183\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=38.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1183\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1183\n",
+       "        json_value: \"14.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c29\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
+       "        value: 80879\n",
+       "      }\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.999943823380709\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 17800\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      min: 5.0\n",
+       "      max: 118.0\n",
+       "      mean: 66.38213483146068\n",
+       "      stddev: 24.980069271880215\n",
+       "      histogram {\n",
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+       "        end: 118.0000118\n",
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+       "        counts: 584\n",
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+       "        counts: 344\n",
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+       "        max: 118.0\n",
+       "        min: 5.0\n",
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+       "        bins: 118.0000118\n",
+       "        n: 17800\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 102.0\n",
+       "        upper: 102.0\n",
+       "        lower: 102.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 5.0\n",
+       "        quantile_values: 9.0\n",
+       "        quantile_values: 20.0\n",
+       "        quantile_values: 50.0\n",
+       "        quantile_values: 69.0\n",
+       "        quantile_values: 82.0\n",
+       "        quantile_values: 107.0\n",
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+       "        quantile_values: 118.0\n",
+       "      }\n",
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+       "      }\n",
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+       "    }\n",
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+       "        json_value: \"72.000000000000000\"\n",
+       "      }\n",
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+       "  }\n",
+       "}\n",
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+       "        json_value: \" 36 months\"\n",
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+       "        estimate: 26174\n",
+       "        json_value: \" 60 months\"\n",
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+       "        json_value: \"term\"\n",
+       "      }\n",
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+       "}\n",
+       "columns {\n",
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+       "  value {\n",
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+       "      type_counts {\n",
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+       "      type_counts {\n",
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+       "        bins: 3.0000003\n",
+       "        bins: 3.3000003299999996\n",
+       "        bins: 3.6000003599999997\n",
+       "        bins: 3.9000003899999998\n",
+       "        bins: 4.200000419999999\n",
+       "        bins: 4.50000045\n",
+       "        bins: 4.80000048\n",
+       "        bins: 5.100000509999999\n",
+       "        bins: 5.40000054\n",
+       "        bins: 5.700000569999999\n",
+       "        bins: 6.0000006\n",
+       "        bins: 6.3000006299999995\n",
+       "        bins: 6.600000659999999\n",
+       "        bins: 6.90000069\n",
+       "        bins: 7.200000719999999\n",
+       "        bins: 7.500000749999999\n",
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+       "        bins: 8.10000081\n",
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+       "        bins: 8.700000869999998\n",
+       "        bins: 9.0000009\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 8.0\n",
+       "        upper: 8.0\n",
+       "        lower: 8.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 1.0\n",
+       "        quantile_values: 2.0\n",
+       "        quantile_values: 9.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
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+       "        doubles {\n",
+       "          estimate: 2\n",
+       "          value: 1.0\n",
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+       "        doubles {\n",
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+       "          value: 2.0\n",
+       "          rank: 2\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=0.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 80879\n",
+       "        json_value: \"0.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=1.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"1.000000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 3158\n",
+       "        json_value: \"2.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=2.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 585\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=3.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 585\n",
+       "        json_value: \"3.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 125\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 125\n",
+       "        json_value: \"5.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 103\n",
+       "        json_value: \"4.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 103\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=4.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 47\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=6.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"6.000000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 46\n",
+       "        json_value: \"9.000000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 46\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=9.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"pub_rec\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c32\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
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+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      max: 405610.0\n",
+       "      mean: 16678.49716758378\n",
+       "      stddev: 20906.30575955345\n",
+       "      histogram {\n",
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+       "        counts: 3264\n",
+       "        counts: 768\n",
+       "        counts: 960\n",
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+       "        counts: 384\n",
+       "        counts: 256\n",
+       "        counts: 64\n",
+       "        counts: 64\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 64\n",
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+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 0\n",
+       "        counts: 256\n",
+       "        max: 405610.0\n",
+       "        bins: 0.0\n",
+       "        bins: 13520.334685366666\n",
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+       "        bins: 405610.040561\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 2019.0\n",
+       "        upper: 2019.0\n",
+       "        lower: 2019.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
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+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 297.0\n",
+       "        quantile_values: 1650.0\n",
+       "        quantile_values: 5578.0\n",
+       "        quantile_values: 11365.0\n",
+       "        quantile_values: 20744.0\n",
+       "        quantile_values: 45007.0\n",
+       "        quantile_values: 115945.0\n",
+       "        quantile_values: 405610.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 6747.0\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 10884.0\n",
+       "          rank: 1\n",
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+       "        doubles {\n",
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+       "          value: 10989.0\n",
+       "          rank: 2\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "          rank: 8\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          rank: 9\n",
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+       "        doubles {\n",
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+       "          value: 80723.0\n",
+       "          rank: 10\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 17665.0\n",
+       "          rank: 11\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=3768.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"2913.000000000000000\"\n",
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+       "        json_value: \"33127.000000000000000\"\n",
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+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"21846.000000000000000\"\n",
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+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=22198.0, stringValue=null)\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15195.0, stringValue=null)\"\n",
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+       "        json_value: \"21790.000000000000000\"\n",
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+       "        json_value: \"5862.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=1222.0, stringValue=null)\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=3945.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c33\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
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+       "      }\n",
+       "      type_counts {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      histogram {\n",
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+       "        n: 98679\n",
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+       "        lower: 851.0\n",
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+       "        quantiles: 0.75\n",
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+       "        quantile_values: 0.0\n",
+       "        quantile_values: 1.7000000476837158\n",
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+       "        quantile_values: 32.70000076293945\n",
+       "        quantile_values: 50.900001525878906\n",
+       "        quantile_values: 72.0\n",
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+       "        quantile_values: 98.5999984741211\n",
+       "        quantile_values: 120.80000305175781\n",
+       "      }\n",
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+       "          value: 89.6\n",
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+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
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+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=71.6, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"64.700000000000003\"\n",
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+       "      }\n",
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+       "        json_value: \"28.399999999999999\"\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c34\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
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+       "        ratio: 0.9999898662342926\n",
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+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
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+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "    number_summary {\n",
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+       "      mean: 24.71555244783591\n",
+       "      stddev: 12.208312557230306\n",
+       "      histogram {\n",
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+       "        end: 99.0000099\n",
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+       "        max: 99.0\n",
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+       "        n: 98679\n",
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+       "      unique_count {\n",
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+       "        lower: 72.0\n",
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+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 3.0\n",
+       "        quantile_values: 5.0\n",
+       "        quantile_values: 8.0\n",
+       "        quantile_values: 16.0\n",
+       "        quantile_values: 23.0\n",
+       "        quantile_values: 31.0\n",
+       "        quantile_values: 47.0\n",
+       "        quantile_values: 60.0\n",
+       "        quantile_values: 99.0\n",
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+       "      frequent_numbers {\n",
+       "        doubles {\n",
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+       "          value: 15.0\n",
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+       "          rank: 10\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
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+       "    frequent_items {\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=21.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"25.000000000000000\"\n",
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+       "        json_value: \"16.000000000000000\"\n",
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+       "        json_value: \"17.000000000000000\"\n",
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+       "        json_value: \"13.000000000000000\"\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c35\"\n",
+       "  value {\n",
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+       "    }\n",
+       "    schema {\n",
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+       "    frequent_items {\n",
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+       "        json_value: \"w\"\n",
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+       "        json_value: \"f\"\n",
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+       "        json_value: \"initial_list_status\"\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "    schema {\n",
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+       "      type_counts {\n",
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+       "      type_counts {\n",
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+       "      }\n",
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+       "    number_summary {\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "      }\n",
+       "      items {\n",
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+       "      items {\n",
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+       "      items {\n",
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+       "      items {\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "    }\n",
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+       "      type_counts {\n",
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+       "        n: 98679\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 28.989999771118164\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "      }\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 2820\n",
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+       "      }\n",
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+       "        estimate: 2635\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2055\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2055\n",
+       "        json_value: \"24000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=35000.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"35000.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=16000.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"16000.000000000000000\"\n",
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+       "        json_value: \"18000.000000000000000\"\n",
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+       "      items {\n",
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+       "      items {\n",
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+       "columns {\n",
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+       "        quantile_values: 21194.2890625\n",
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+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 12\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
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+       "      }\n",
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+       "        json_value: \"30.000000000000000\"\n",
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+       "        json_value: \"45.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=45.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=60.63, stringValue=null)\"\n",
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+       "        json_value: \"60.630000000000003\"\n",
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+       "        json_value: \"31.520000000000000\"\n",
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+       "        json_value: \"28.210000000000001\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=28.21, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"26.410000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=26.41, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"25.719999999999999\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"26.070000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=26.07, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=173.61, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"70.819999999999993\"\n",
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+       "        json_value: \"32.149999999999999\"\n",
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+       "      }\n",
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+       "      type_counts {\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "        json_value: \"Mar-2019\"\n",
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+       "        json_value: \"Feb-2019\"\n",
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+       "        json_value: \"May-2017\"\n",
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+       "        json_value: \"Jan-2017\"\n",
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+       "        json_value: \"Nov-2017\"\n",
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+       "        json_value: \"May-2018\"\n",
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+       "        json_value: \"Aug-2018\"\n",
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+       "        json_value: \"Jul-2018\"\n",
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+       "        json_value: \"Dec-2016\"\n",
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+       "        estimate: 1940\n",
+       "        json_value: \"Mar-2018\"\n",
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+       "        estimate: 1873\n",
+       "        json_value: \"Jul-2017\"\n",
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+       "        json_value: \"Apr-2018\"\n",
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+       "        json_value: \"Oct-2018\"\n",
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+       "columns {\n",
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+       "        quantile_values: 27716.7109375\n",
+       "        quantile_values: 35681.55078125\n",
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+       "columns {\n",
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+       "        json_value: \"next_pymnt_d\"\n",
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+       "columns {\n",
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+       "        json_value: \"Mar-2019\"\n",
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+       "        json_value: \"Jan-2018\"\n",
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+       "        json_value: \"May-2017\"\n",
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+       "        json_value: \"Mar-2017\"\n",
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+       "        json_value: \"Jul-2017\"\n",
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+       "        json_value: \"Dec-2016\"\n",
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+       "        json_value: \"Jun-2018\"\n",
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+       "        json_value: \"Nov-2016\"\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "    }\n",
+       "    schema {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
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+       "    number_summary {\n",
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+       "      }\n",
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+       "        quantile_values: 850.0\n",
+       "      }\n",
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+       "      }\n",
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+       "  }\n",
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+       "columns {\n",
+       "  key: \"_c5\"\n",
+       "  value {\n",
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+       "    }\n",
+       "    schema {\n",
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+       "      type_counts {\n",
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+       "      type_counts {\n",
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+       "    number_summary {\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 1391.4100341796875\n",
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+       "        bins: 3.333333666666667\n",
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+       "        bins: 20.000002000000002\n",
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+       "        bins: 30.000003000000003\n",
+       "        bins: 33.33333666666667\n",
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+       "        bins: 40.000004000000004\n",
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+       "        bins: 66.66667333333334\n",
+       "        bins: 70.00000700000001\n",
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+       "        bins: 80.00000800000001\n",
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+       "        bins: 90.000009\n",
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+       "        bins: 96.66667633333334\n",
+       "        bins: 100.00001\n",
+       "        n: 21344\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 91.0\n",
+       "        upper: 91.0\n",
+       "        lower: 91.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 3.0\n",
+       "        quantile_values: 9.0\n",
+       "        quantile_values: 25.0\n",
+       "        quantile_values: 42.0\n",
+       "        quantile_values: 63.0\n",
+       "        quantile_values: 78.0\n",
+       "        quantile_values: 88.0\n",
+       "        quantile_values: 100.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 3\n",
+       "          value: 68.0\n",
+       "        }\n",
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+       "        doubles {\n",
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+       "        }\n",
+       "        doubles {\n",
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+       "        }\n",
+       "        doubles {\n",
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+       "          value: 61.0\n",
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+       "        }\n",
+       "        doubles {\n",
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+       "        doubles {\n",
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+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
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+       "        estimate: 628\n",
+       "        json_value: \"46.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=46.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=36.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"36.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"20.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 527\n",
+       "        json_value: \"26.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=26.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"40.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=40.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 521\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=52.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 521\n",
+       "        json_value: \"52.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 518\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=68.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 517\n",
+       "        json_value: \"68.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 516\n",
+       "        json_value: \"41.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 516\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=41.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 514\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=74.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 513\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=29.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 513\n",
+       "        json_value: \"74.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 512\n",
+       "        json_value: \"29.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 509\n",
+       "        json_value: \"15.000000000000000\"\n",
+       "      }\n",
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+       "        estimate: 508\n",
+       "        json_value: \"79.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=79.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 506\n",
+       "        json_value: \"25.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=73.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 506\n",
+       "        json_value: \"73.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=38.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 505\n",
+       "        json_value: \"38.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 505\n",
+       "        json_value: \"47.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c53\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
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+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
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+       "      mean: 1.0\n",
+       "      histogram {\n",
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+       "        end: 1.0000001\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 1.0\n",
+       "        upper: 1.0\n",
+       "        lower: 1.0\n",
+       "      }\n",
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+       "        quantiles: 0.0\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 1.0\n",
+       "        quantile_values: 1.0\n",
+       "        quantile_values: 1.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 12\n",
+       "          value: 1.0\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 98679\n",
+       "        json_value: \"1.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"policy_code\"\n",
+       "      }\n",
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+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c54\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: STRING\n",
+       "        ratio: 1.0\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 98680\n",
+       "      }\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 96113\n",
+       "        json_value: \"Individual\"\n",
+       "      }\n",
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+       "        estimate: 2566\n",
+       "        json_value: \"Joint App\"\n",
+       "      }\n",
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+       "        estimate: 1\n",
+       "        json_value: \"application_type\"\n",
+       "      }\n",
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+       "columns {\n",
+       "  key: \"_c55\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
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+       "      }\n",
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+       "    schema {\n",
+       "      inferred_type {\n",
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+       "      }\n",
+       "      type_counts {\n",
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+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
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+       "    number_summary {\n",
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+       "      mean: 99509.808558067\n",
+       "      stddev: 32987.66673550849\n",
+       "      histogram {\n",
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+       "        n: 2566\n",
+       "      }\n",
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+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.5\n",
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+       "        quantiles: 1.0\n",
+       "        quantile_values: 27000.0\n",
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+       "        quantile_values: 45532.0\n",
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+       "        quantile_values: 124000.0\n",
+       "        quantile_values: 155000.0\n",
+       "        quantile_values: 194400.0\n",
+       "        quantile_values: 194400.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 2\n",
+       "          value: 155000.0\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 101400.0\n",
+       "          rank: 1\n",
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+       "          rank: 4\n",
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+       "          rank: 5\n",
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+       "        doubles {\n",
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+       "          value: 137000.0\n",
+       "          rank: 9\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 177\n",
+       "        json_value: \"110000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 177\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=110000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=90000.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"80000.000000000000000\"\n",
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+       "        json_value: \"90000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=80000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 83\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=75000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 83\n",
+       "        json_value: \"75000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 79\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=104000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 79\n",
+       "        json_value: \"104000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 62\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=76000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 62\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=87000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 62\n",
+       "        json_value: \"87000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 59\n",
+       "        json_value: \"76000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 58\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=81000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 57\n",
+       "        json_value: \"81000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 57\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=114000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 57\n",
+       "        json_value: \"114000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 56\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=167500.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 56\n",
+       "        json_value: \"167500.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 56\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=78320.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 55\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=127000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 55\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=91000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 55\n",
+       "        json_value: \"127000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 55\n",
+       "        json_value: \"91000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 54\n",
+       "        json_value: \"83000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 54\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=83000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 54\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=101400.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 53\n",
+       "        json_value: \"45532.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 53\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=45532.0, stringValue=null)\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c56\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
+       "        value: 96113\n",
+       "      }\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9996104402025711\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 2566\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
+       "      count: 2566\n",
+       "      min: 7.47\n",
+       "      max: 29.19\n",
+       "      mean: 17.871173031956356\n",
+       "      stddev: 5.427174795856968\n",
+       "      histogram {\n",
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+       "        counts: 0\n",
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+       "        counts: 0\n",
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+       "        counts: 39\n",
+       "        counts: 99\n",
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+       "        counts: 0\n",
+       "        counts: 205\n",
+       "        counts: 96\n",
+       "        counts: 0\n",
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+       "        counts: 120\n",
+       "        counts: 99\n",
+       "        counts: 56\n",
+       "        counts: 38\n",
+       "        counts: 83\n",
+       "        counts: 109\n",
+       "        counts: 0\n",
+       "        counts: 56\n",
+       "        max: 29.190000534057617\n",
+       "        min: 7.46999979019165\n",
+       "        bins: 7.46999979019165\n",
+       "        bins: 8.193999912287184\n",
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+       "        bins: 27.018003086771067\n",
+       "        bins: 27.742003208866603\n",
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+       "        bins: 29.19000345305767\n",
+       "        n: 2566\n",
+       "      }\n",
+       "      unique_count {\n",
+       "        estimate: 54.0\n",
+       "        upper: 54.0\n",
+       "        lower: 54.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 7.46999979019165\n",
+       "        quantile_values: 7.46999979019165\n",
+       "        quantile_values: 9.989999771118164\n",
+       "        quantile_values: 13.510000228881836\n",
+       "        quantile_values: 16.5\n",
+       "        quantile_values: 21.6200008392334\n",
+       "        quantile_values: 27.56999969482422\n",
+       "        quantile_values: 29.190000534057617\n",
+       "        quantile_values: 29.190000534057617\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 2\n",
+       "          value: 16.36\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 2\n",
+       "          value: 19.53\n",
+       "          rank: 1\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 11.7\n",
+       "          rank: 2\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 14.63\n",
+       "          rank: 3\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 13.92\n",
+       "          rank: 4\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 9.99\n",
+       "          rank: 5\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          value: 17.06\n",
+       "          rank: 6\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 23.88\n",
+       "          rank: 7\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 16.31\n",
+       "          rank: 8\n",
+       "        }\n",
+       "        doubles {\n",
+       "          estimate: 1\n",
+       "          value: 17.77\n",
+       "          rank: 9\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 75\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=23.41, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 75\n",
+       "        json_value: \"23.410000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"21.100000000000001\"\n",
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+       "        json_value: \"17.770000000000000\"\n",
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+       "      items {\n",
+       "        estimate: 70\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=17.77, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 68\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=21.1, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 67\n",
+       "        json_value: \"24.329999999999998\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"13.420000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24.85, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 67\n",
+       "        json_value: \"13.449999999999999\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=8.64, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 67\n",
+       "        json_value: \"12.190000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=13.42, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 66\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=24.33, stringValue=null)\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=11.71, stringValue=null)\"\n",
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+       "        estimate: 66\n",
+       "        json_value: \"8.640000000000001\"\n",
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+       "        json_value: \"24.850000000000001\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=19.5, stringValue=null)\"\n",
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+       "      items {\n",
+       "        estimate: 65\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=13.45, stringValue=null)\"\n",
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+       "        estimate: 65\n",
+       "        json_value: \"13.920000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=27.57, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 65\n",
+       "        json_value: \"18.489999999999998\"\n",
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+       "      items {\n",
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+       "        json_value: \"11.170000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20.53, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 65\n",
+       "        json_value: \"19.500000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14.34, stringValue=null)\"\n",
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+       "      items {\n",
+       "        estimate: 64\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
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+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 2566\n",
+       "        json_value: \"Not Verified\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"verification_status_joint\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
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+       "      count: 98680\n",
+       "    }\n",
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+       "      type_counts {\n",
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+       "      type_counts {\n",
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+       "        value: 1\n",
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+       "    number_summary {\n",
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+       "      histogram {\n",
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+       "        n: 98679\n",
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+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
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+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 2.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
+       "          estimate: 12\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
+       "        estimate: 98195\n",
+       "        json_value: \"0.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 98195\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=0.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"1.000000000000000\"\n",
+       "      }\n",
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+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=28904.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
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+       "    }\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        lower: 116.0\n",
+       "      }\n",
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+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.5\n",
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+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
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+       "        quantile_values: 91.0\n",
+       "        quantile_values: 104.0\n",
+       "        quantile_values: 158.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "        json_value: \"64.000000000000000\"\n",
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+       "        json_value: \"65.000000000000000\"\n",
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+       "  }\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "      type_counts {\n",
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+       "    number_summary {\n",
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+       "        quantiles: 0.95\n",
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+       "        quantile_values: 158600.0\n",
+       "        quantile_values: 509700.0\n",
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+       "        json_value: \"33000.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=20000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=49400.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=33900.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"18000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=25500.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"30000.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15700.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=15900.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=28500.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"28800.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"25500.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"30800.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=79700.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"14900.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=78700.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"4100.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=10200.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=28900.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"24100.000000000000000\"\n",
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+       "        json_value: \"56700.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=16500.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 2035\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=30000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
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+       "        json_value: \"28400.000000000000000\"\n",
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+       "      items {\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
+       "    counters {\n",
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+       "    }\n",
+       "    schema {\n",
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+       "      }\n",
+       "      type_counts {\n",
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+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      mean: 4.739853464262913\n",
+       "      stddev: 3.3682111952189002\n",
+       "      histogram {\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        upper: 23.0\n",
+       "        lower: 23.0\n",
+       "      }\n",
+       "      quantiles {\n",
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+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
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+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
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+       "        quantile_values: 2.0\n",
+       "        quantile_values: 4.0\n",
+       "        quantile_values: 6.0\n",
+       "        quantile_values: 11.0\n",
+       "        quantile_values: 16.0\n",
+       "        quantile_values: 28.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          value: 2.0\n",
+       "        }\n",
+       "        doubles {\n",
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+       "        }\n",
+       "        doubles {\n",
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+       "          value: 6.0\n",
+       "          rank: 7\n",
+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
+       "      items {\n",
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+       "      }\n",
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+       "        json_value: \"3.000000000000000\"\n",
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+       "        json_value: \"4.000000000000000\"\n",
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+       "      }\n",
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+       "        json_value: \"2.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"11.000000000000000\"\n",
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+       "        json_value: \"13.000000000000000\"\n",
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+       "columns {\n",
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+       "  value {\n",
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+       "    }\n",
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+       "      type_counts {\n",
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+       "    number_summary {\n",
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+       "        quantile_values: 68336.0\n",
+       "        quantile_values: 171635.0\n",
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+       "      }\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
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+       "      count: 98680\n",
+       "      true_count {\n",
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+       "      }\n",
+       "    }\n",
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+       "      type_counts {\n",
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+       "        value: 1\n",
+       "      }\n",
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+       "      }\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 1588.0\n",
+       "        quantile_values: 4909.0\n",
+       "        quantile_values: 12962.0\n",
+       "        quantile_values: 43088.0\n",
+       "        quantile_values: 86628.0\n",
+       "        quantile_values: 214722.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          value: 9278.0\n",
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+       "        json_value: \"0.000000000000000\"\n",
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+       "        estimate: 2021\n",
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+       "      items {\n",
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+       "      items {\n",
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+       "      }\n",
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+       "      }\n",
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+       "      }\n",
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+       "      type_counts {\n",
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+       "      }\n",
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+       "        }\n",
+       "      }\n",
+       "      is_discrete: true\n",
+       "    }\n",
+       "    frequent_items {\n",
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+       "        json_value: \"163.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1987\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=163.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"127.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=127.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"129.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1974\n",
+       "        json_value: \"119.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1974\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=119.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1974\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=138.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1973\n",
+       "        json_value: \"138.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"151.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=151.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=142.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=115.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=140.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"142.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1971\n",
+       "        json_value: \"115.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1970\n",
+       "        json_value: \"92.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=92.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=114.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1970\n",
+       "        json_value: \"144.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1970\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=149.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1970\n",
+       "        json_value: \"149.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
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+       "        estimate: 1970\n",
+       "        json_value: \"134.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1970\n",
+       "        json_value: \"140.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"114.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 1969\n",
+       "        json_value: \"122.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
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+       "    }\n",
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+       "          value: 367.0\n",
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+       "      }\n",
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+       "  }\n",
+       "}\n",
+       "columns {\n",
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+       "  value {\n",
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+       "        n: 98679\n",
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+       "        quantile_values: 227.0\n",
+       "      }\n",
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+       "      unique_count {\n",
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+       "        lower: 147.0\n",
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+       "        quantiles: 0.0\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 92.0\n",
+       "        quantile_values: 146.0\n",
+       "        quantile_values: 381.0\n",
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+       "        doubles {\n",
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+       "      is_discrete: true\n",
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+       "        json_value: \"5.000000000000000\"\n",
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+       "        json_value: \"2.000000000000000\"\n",
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+       "        json_value: \"4.000000000000000\"\n",
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+       "        json_value: \"11.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=1.0, stringValue=null)\"\n",
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+       "        json_value: \"1.000000000000000\"\n",
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+       "      items {\n",
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+       "        json_value: \"12.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=12.0, stringValue=null)\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=14.0, stringValue=null)\"\n",
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+       "        estimate: 2699\n",
+       "        json_value: \"14.000000000000000\"\n",
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+       "        json_value: \"15.000000000000000\"\n",
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+       "  }\n",
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+       "columns {\n",
+       "  key: \"_c75\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "      true_count {\n",
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+       "      }\n",
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+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999545702344176\n",
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+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
+       "        value: 22011\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
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+       "    number_summary {\n",
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+       "      max: 90.0\n",
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+       "        n: 22011\n",
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+       "      unique_count {\n",
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+       "        upper: 86.0\n",
+       "        lower: 86.0\n",
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+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
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+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
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+       "        quantile_values: 83.0\n",
+       "        quantile_values: 90.0\n",
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+       "      frequent_numbers {\n",
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+       "          value: 6.0\n",
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+       "      type_counts {\n",
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+       "        json_value: \"14.000000000000000\"\n",
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+       "columns {\n",
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+       "      true_count {\n",
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+       "      }\n",
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+       "    schema {\n",
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+       "      type_counts {\n",
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+       "        estimate: 1957\n",
+       "        json_value: \"Manager\"\n",
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+       "        estimate: 1595\n",
+       "        json_value: \"Teacher\"\n",
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+       "        estimate: 1255\n",
+       "        json_value: \"Owner\"\n",
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+       "        json_value: \"RN\"\n",
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+       "        json_value: \"President\"\n",
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+       "        json_value: \"Driver\"\n",
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+       "        json_value: \"Sales\"\n",
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+       "        json_value: \"General Manager\"\n",
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+       "        json_value: \"Registered Nurse\"\n",
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+       "        json_value: \"Engineer\"\n",
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+       "        json_value: \"Full lecturer\"\n",
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+       "        json_value: \"Compliance Manager\"\n",
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+       "        json_value: \"Pool construction\"\n",
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+       "        json_value: \"Sales Manager\"\n",
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+       "        json_value: \"Supervisor\"\n",
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+       "        json_value: \"Letter Carrier\"\n",
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+       "        json_value: \"Senior Program Associate\"\n",
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+       "        json_value: \"Team Supervisor\"\n",
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+       "        json_value: \"Title examiner\"\n",
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+       "        json_value: \"line cook\"\n",
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+       "        json_value: \"Police Officer\"\n",
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+       "        json_value: \"Administrator\"\n",
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+       "        json_value: \"P&D Driver\"\n",
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+       "        json_value: \"Executive Assistant\"\n",
+       "      }\n",
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+       "        json_value: \"WAREHOUSE ASSOCIATE \"\n",
+       "      }\n",
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+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c80\"\n",
+       "  value {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "columns {\n",
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+       "      type_counts {\n",
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+       "        n: 98679\n",
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+       "        quantile_values: 25.0\n",
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+       "        bins: 9.533334286666667\n",
+       "        bins: 10.266667693333334\n",
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+       "        bins: 11.733334506666667\n",
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+       "        bins: 13.20000132\n",
+       "        bins: 13.933334726666667\n",
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+       "        bins: 15.40000154\n",
+       "        bins: 16.13333494666667\n",
+       "        bins: 16.866668353333335\n",
+       "        bins: 17.60000176\n",
+       "        bins: 18.333335166666668\n",
+       "        bins: 19.066668573333335\n",
+       "        bins: 19.80000198\n",
+       "        bins: 20.533335386666668\n",
+       "        bins: 21.266668793333334\n",
+       "        bins: 22.0000022\n",
+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        upper: 10.0\n",
+       "        lower: 10.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 1.0\n",
+       "        quantile_values: 2.0\n",
+       "        quantile_values: 22.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          estimate: 12\n",
+       "        }\n",
+       "      }\n",
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+       "    }\n",
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+       "        json_value: \"2.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=2.0, stringValue=null)\"\n",
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+       "        estimate: 265\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=3.0, stringValue=null)\"\n",
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+       "        json_value: \"3.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=4.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 94\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=6.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 94\n",
+       "        json_value: \"6.000000000000000\"\n",
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+       "        json_value: \"22.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=22.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=5.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        estimate: 49\n",
+       "        json_value: \"5.000000000000000\"\n",
+       "      }\n",
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+       "        estimate: 46\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=10.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"10.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=12.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"num_tl_90g_dpd_24m\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c91\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "        ratio: 0.9999898662342926\n",
+       "      }\n",
+       "      type_counts {\n",
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+       "        value: 98679\n",
+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
+       "      }\n",
+       "    }\n",
+       "    number_summary {\n",
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+       "      max: 18.0\n",
+       "      mean: 2.245016670213521\n",
+       "      stddev: 1.9150729166286458\n",
+       "      histogram {\n",
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+       "        counts: 0\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        lower: 14.0\n",
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+       "        quantiles: 0.5\n",
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+       "        quantiles: 0.95\n",
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+       "        quantiles: 1.0\n",
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+       "        quantile_values: 2.0\n",
+       "        quantile_values: 3.0\n",
+       "        quantile_values: 6.0\n",
+       "        quantile_values: 8.0\n",
+       "        quantile_values: 18.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          value: 3.0\n",
+       "        }\n",
+       "        doubles {\n",
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+       "          rank: 1\n",
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+       "        doubles {\n",
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+       "          value: 1.0\n",
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+       "        doubles {\n",
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+       "        doubles {\n",
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+       "          value: 5.0\n",
+       "          rank: 5\n",
+       "        }\n",
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+       "        json_value: \"1.000000000000000\"\n",
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+       "        json_value: \"2.000000000000000\"\n",
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+       "        json_value: \"0.000000000000000\"\n",
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+       "        json_value: \"9.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=9.0, stringValue=null)\"\n",
+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"10.000000000000000\"\n",
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+       "        json_value: \"11.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=11.0, stringValue=null)\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=13.0, stringValue=null)\"\n",
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+       "        json_value: \"18.000000000000000\"\n",
+       "      }\n",
+       "      items {\n",
+       "        estimate: 1\n",
+       "        json_value: \"num_tl_op_past_12m\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c92\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
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+       "      type_counts {\n",
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+       "      }\n",
+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "      }\n",
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+       "    number_summary {\n",
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+       "        bins: 26.700000762939453\n",
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+       "        quantiles: 0.5\n",
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+       "        quantile_values: 62.5\n",
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+       "        quantile_values: 91.0\n",
+       "        quantile_values: 100.0\n",
+       "        quantile_values: 100.0\n",
+       "        quantile_values: 100.0\n",
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+       "        quantile_values: 100.0\n",
+       "      }\n",
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+       "      }\n",
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+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"87.500000000000000\"\n",
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+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
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+       "  value {\n",
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+       "      count: 98680\n",
+       "      true_count {\n",
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+       "      }\n",
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+       "        ratio: 0.999989794356279\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "      }\n",
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+       "        n: 97984\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        lower: 59.0\n",
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+       "        quantile_values: 100.0\n",
+       "        quantile_values: 100.0\n",
+       "        quantile_values: 100.0\n",
+       "      }\n",
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+       "      type_counts {\n",
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+       "          estimate: 11\n",
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+       "        json_value: \"pub_rec_bankruptcies\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
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+       "      type_counts {\n",
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+       "columns {\n",
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+       "      type_counts {\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        upper: 2070.0\n",
+       "        lower: 2070.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
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+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 232.0\n",
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+       "        quantile_values: 564712.0\n",
+       "      }\n",
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+       "        doubles {\n",
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+       "      }\n",
+       "      is_discrete: true\n",
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+       "        json_value: \"55186.000000000000000\"\n",
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+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c98\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
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+       "      type_counts {\n",
+       "        key: \"FRACTIONAL\"\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
+       "        value: 1\n",
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+       "    number_summary {\n",
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+       "        n: 98679\n",
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+       "      unique_count {\n",
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+       "        upper: 633.0\n",
+       "        lower: 633.0\n",
+       "      }\n",
+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
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+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
+       "        quantile_values: 0.0\n",
+       "        quantile_values: 400.0\n",
+       "        quantile_values: 2300.0\n",
+       "        quantile_values: 8000.0\n",
+       "        quantile_values: 15800.0\n",
+       "        quantile_values: 28600.0\n",
+       "        quantile_values: 65900.0\n",
+       "        quantile_values: 115200.0\n",
+       "        quantile_values: 215200.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
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+       "          estimate: 1\n",
+       "          value: 3000.0\n",
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+       "        doubles {\n",
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+       "          rank: 11\n",
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+       "      is_discrete: true\n",
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+       "    frequent_items {\n",
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+       "        json_value: \"7000.000000000000000\"\n",
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+       "        json_value: \"24800.000000000000000\"\n",
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+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=19000.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=4100.0, stringValue=null)\"\n",
+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=8000.0, stringValue=null)\"\n",
+       "      }\n",
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+       "      }\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=28500.0, stringValue=null)\"\n",
+       "      }\n",
+       "      items {\n",
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+       "        json_value: \"10900.000000000000000\"\n",
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+       "        json_value: \"21400.000000000000000\"\n",
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+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=22600.0, stringValue=null)\"\n",
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+       "      }\n",
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+       "        json_value: \"22600.000000000000000\"\n",
+       "      }\n",
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+       "        json_value: \"3500.000000000000000\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "columns {\n",
+       "  key: \"_c99\"\n",
+       "  value {\n",
+       "    counters {\n",
+       "      count: 98680\n",
+       "    }\n",
+       "    schema {\n",
+       "      inferred_type {\n",
+       "        type: FRACTIONAL\n",
+       "        ratio: 0.9999898662342926\n",
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+       "      type_counts {\n",
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+       "      type_counts {\n",
+       "        key: \"STRING\"\n",
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+       "    number_summary {\n",
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+       "        max: 424572.0\n",
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+       "        n: 98679\n",
+       "      }\n",
+       "      unique_count {\n",
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+       "        upper: 1748.0\n",
+       "        lower: 1748.0\n",
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+       "      quantiles {\n",
+       "        quantiles: 0.0\n",
+       "        quantiles: 0.01\n",
+       "        quantiles: 0.05\n",
+       "        quantiles: 0.25\n",
+       "        quantiles: 0.5\n",
+       "        quantiles: 0.75\n",
+       "        quantiles: 0.95\n",
+       "        quantiles: 0.99\n",
+       "        quantiles: 1.0\n",
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+       "        quantile_values: 33338.0\n",
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+       "        quantile_values: 131001.0\n",
+       "        quantile_values: 210421.0\n",
+       "        quantile_values: 424572.0\n",
+       "      }\n",
+       "      frequent_numbers {\n",
+       "        doubles {\n",
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+       "        doubles {\n",
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+       "      is_discrete: true\n",
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+       "    frequent_items {\n",
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+       "        json_value: \"34850.000000000000000\"\n",
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+       "      }\n",
+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 1841\n",
+       "        json_value: \"20148.000000000000000\"\n",
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+       "        json_value: \"16484.000000000000000\"\n",
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+       "      items {\n",
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+       "      }\n",
+       "      items {\n",
+       "        estimate: 1841\n",
+       "        json_value: \"TypedData(type=FRACTIONAL, booleanValue=false, integralValue=0, fractional=38438.0, stringValue=null)\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf['profile'].get(0)[0].to_summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "spark",
+   "language": "python",
+   "name": "spark"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
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+}