{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">### 🚩 *Create a free WhyLabs account to get more value out of whylogs!*<br> \n",
    ">*Did you know you can store, visualize, and monitor whylogs profiles with the [WhyLabs Observability Platform](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Getting_Started)? Sign up for a [free WhyLabs account](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Getting_Started) to leverage the power of whylogs and WhyLabs together!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started_with_UDFs.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we'll explore the basics of logging data with whylogs and a user defined function or UDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing whylogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install whylogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading a Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before showing how we can log data, we first need the data itself. Let's create a simple Pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = {\n",
    "    \"animal\": [\"cat\", \"hawk\", \"clam\", \"cat\", \"mongoose\", \"octopus\"],\n",
    "    \"legs\": [4, 2, 1, 4, 4, 8],\n",
    "    \"weight\": [4.3, 1.8, 1.3, 4.1, 5.4, 3.2],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a simple metric UDF\n",
    "Here we use a metric UDF targeting a named column `animal` as an example to show how we can add features to a dataframe for custom monitoring. In this example we model some custom logic for if the animal has a cool name. This is a toy example that just checks if the name is longer than 4 characters, and does a binary classification, but you could return a score based on values in a column too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import whylogs as why\n",
    "from whylogs.experimental.core.udf_schema import udf_schema\n",
    "from whylogs.experimental.core.metrics.udf_metric import register_metric_udf\n",
    "\n",
    "\n",
    "@register_metric_udf(col_name=\"animal\")\n",
    "def has_cool_animal_name(text):\n",
    "  if len(text) > 4: # long names are cool\n",
    "    return 1\n",
    "  else:\n",
    "    return 0\n",
    "  \n",
    "custom_schema = udf_schema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profiling with whylogs + UDFs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain a profile of your data, you can simply use whylogs' `log` call with your UDF schema defined earlier. This will attach a feature named `animal.has_cool_animal_name` which you can then see in WhyLabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import whylogs as why\n",
    "\n",
    "why.init()\n",
    "\n",
    "results = why.log(df, name=\"udf_demo\", schema=custom_schema)\n",
    "results.view().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going Further with UDFs\n",
    "Unlike metric UDFs, **dataset UDFs** can take multiple columns as input. Dataset UDFs create a new column in your pandas dataframe, which then is profiled along with your inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from whylogs.experimental.core.udf_schema import register_dataset_udf\n",
    "import pandas as pd\n",
    "\n",
    "@register_dataset_udf([\"legs\", \"weight\"])\n",
    "def weight_per_leg(data: pd.DataFrame) -> pd.Series:\n",
    "    return data[\"weight\"] / data[\"legs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_schema2 = udf_schema()\n",
    "results = why.log(df, schema=custom_schema2)\n",
    "results.view().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details on the different kinds of UDFs (say you wanted to calculate a score based on multiple columns) see this example:\n",
    "* https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/whylogs_UDF_examples.ipynb"
   ]
  }
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