docs/source/examples/online_retail_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Demo Notebook for Online Retail Analysis\n",
    "\n",
    "#### [download notebook](https://github.com/opensearch-project/opensearch-py-ml/blob/main/docs/source/examples/online_retail_analysis.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import this to stop opensearch-py-ml from yelling every time a DataFrame connection made\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:06.764412Z",
     "iopub.status.busy": "2021-12-15T20:25:06.755567Z",
     "iopub.status.idle": "2021-12-15T20:25:07.316950Z",
     "shell.execute_reply": "2021-12-15T20:25:07.316561Z"
    }
   },
   "outputs": [],
   "source": [
    "# imports to demonstrate DataFrame support\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import opensearch_py_ml as oml\n",
    "from opensearchpy import OpenSearch\n",
    "\n",
    "# Import standard test settings for consistent results\n",
    "from opensearch_py_ml.conftest import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Setup clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLUSTER_URL = 'https://localhost:9200'\n",
    "\n",
    "def get_os_client(cluster_url = CLUSTER_URL,\n",
    "                  username='admin',\n",
    "                  password='< admin password >'):\n",
    "    '''\n",
    "    Get OpenSearch client\n",
    "    :param cluster_url: cluster URL like https://ml-te-netwo-1s12ba42br23v-ff1736fa7db98ff2.elb.us-west-2.amazonaws.com:443\n",
    "    :return: OpenSearch client\n",
    "    '''\n",
    "    client = OpenSearch(\n",
    "        hosts=[cluster_url],\n",
    "        http_auth=(username, password),\n",
    "        verify_certs=False\n",
    "    )\n",
    "    return client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = get_os_client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "To get started, let's create an `opensearch_py_ml.DataFrame` by reading a csv file. This creates and populates the \n",
    "`online-retail` index in the local Opensearch cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:07.324283Z",
     "iopub.status.busy": "2021-12-15T20:25:07.323764Z",
     "iopub.status.idle": "2021-12-15T20:25:16.241379Z",
     "shell.execute_reply": "2021-12-15T20:25:16.241877Z"
    }
   },
   "outputs": [],
   "source": [
    "df = oml.csv_to_opensearch(\"data/online-retail.csv.gz\",\n",
    "                     os_client=client, \n",
    "                     os_dest_index='online-retail', \n",
    "                     es_if_exists='replace', \n",
    "                     os_dropna=True,\n",
    "                     es_refresh=True,\n",
    "                     compression='gzip',\n",
    "                     index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the `\"_id\"` field was used to index our data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:16.246737Z",
     "iopub.status.busy": "2021-12-15T20:25:16.244084Z",
     "iopub.status.idle": "2021-12-15T20:25:16.250080Z",
     "shell.execute_reply": "2021-12-15T20:25:16.250410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'_id'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.os_index_field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can check which field from opensearch are available to our opensearch_py_ml data frame. `columns` is available as a parameter when instantiating the data frame which allows one to choose only a subset of fields from your index to be included in the data frame. Since we didn't set this parameter, we have access to all fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:16.254703Z",
     "iopub.status.busy": "2021-12-15T20:25:16.254060Z",
     "iopub.status.idle": "2021-12-15T20:25:16.256567Z",
     "shell.execute_reply": "2021-12-15T20:25:16.256138Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode',\n",
       "       'UnitPrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's see the data types of our fields. Running `df.dtypes`, we can see that opensearch field types are mapped to pandas field types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:16.261335Z",
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     "shell.execute_reply": "2021-12-15T20:25:16.263323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country         object\n",
       "CustomerID     float64\n",
       "Description     object\n",
       "InvoiceDate     object\n",
       "InvoiceNo       object\n",
       "Quantity         int64\n",
       "StockCode       object\n",
       "UnitPrice      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also offer a `.os_info()` data frame method that shows all info about the underlying index. It also contains information about operations being passed from data frame methods to opensearch. More on this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:16.266245Z",
     "iopub.status.busy": "2021-12-15T20:25:16.265860Z",
     "iopub.status.idle": "2021-12-15T20:25:16.271135Z",
     "shell.execute_reply": "2021-12-15T20:25:16.270816Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os_index_pattern: online-retail\n",
      "Index:\n",
      " os_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "            os_field_name  is_source os_dtype os_date_format pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_os_field_name\n",
      "Country           Country       True  keyword           None   object           True             True        False                    Country\n",
      "CustomerID     CustomerID       True   double           None  float64           True             True        False                 CustomerID\n",
      "Description   Description       True  keyword           None   object           True             True        False                Description\n",
      "InvoiceDate   InvoiceDate       True  keyword           None   object           True             True        False                InvoiceDate\n",
      "InvoiceNo       InvoiceNo       True  keyword           None   object           True             True        False                  InvoiceNo\n",
      "Quantity         Quantity       True     long           None    int64           True             True        False                   Quantity\n",
      "StockCode       StockCode       True  keyword           None   object           True             True        False                  StockCode\n",
      "UnitPrice       UnitPrice       True   double           None  float64           True             True        False                  UnitPrice\n",
      "Operations:\n",
      " tasks: []\n",
      " size: None\n",
      " sort_params: None\n",
      " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n",
      " body: {}\n",
      " post_processing: []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.os_info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting and Indexing Data\n",
    "\n",
    "Now that we understand how to create a data frame and get access to it's underlying attributes, let's see how we can select subsets of our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### head and tail\n",
    "\n",
    "much like pandas, opensearch_py_ml data frames offer `.head(n)` and `.tail(n)` methods that return the first and last n rows, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:16.274779Z",
     "iopub.status.busy": "2021-12-15T20:25:16.274393Z",
     "iopub.status.idle": "2021-12-15T20:25:17.555325Z",
     "shell.execute_reply": "2021-12-15T20:25:17.555642Z"
    }
   },
   "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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85123A</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71053</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>2 rows × 8 columns</p>"
      ],
      "text/plain": [
       "          Country  CustomerID  ... StockCode UnitPrice\n",
       "0  United Kingdom     17850.0  ...    85123A      2.55\n",
       "1  United Kingdom     17850.0  ...     71053      3.39\n",
       "\n",
       "[2 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:17.559534Z",
     "iopub.status.busy": "2021-12-15T20:25:17.559123Z",
     "iopub.status.idle": "2021-12-15T20:25:17.637500Z",
     "shell.execute_reply": "2021-12-15T20:25:17.637125Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os_index_pattern: online-retail\n",
      "Index:\n",
      " os_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "            os_field_name  is_source os_dtype os_date_format pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_os_field_name\n",
      "Country           Country       True  keyword           None   object           True             True        False                    Country\n",
      "CustomerID     CustomerID       True   double           None  float64           True             True        False                 CustomerID\n",
      "Description   Description       True  keyword           None   object           True             True        False                Description\n",
      "InvoiceDate   InvoiceDate       True  keyword           None   object           True             True        False                InvoiceDate\n",
      "InvoiceNo       InvoiceNo       True  keyword           None   object           True             True        False                  InvoiceNo\n",
      "Quantity         Quantity       True     long           None    int64           True             True        False                   Quantity\n",
      "StockCode       StockCode       True  keyword           None   object           True             True        False                  StockCode\n",
      "UnitPrice       UnitPrice       True   double           None  float64           True             True        False                  UnitPrice\n",
      "Operations:\n",
      " tasks: [('tail': ('sort_field': '_doc', 'count': 2)), ('head': ('sort_field': '_doc', 'count': 2)), ('tail': ('sort_field': '_doc', 'count': 2))]\n",
      " size: 2\n",
      " sort_params: {'_doc': 'desc'}\n",
      " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n",
      " body: {}\n",
      " post_processing: [('sort_index'), ('head': ('count': 2)), ('tail': ('count': 2))]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.tail(2).head(2).tail(2).os_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:17.640519Z",
     "iopub.status.busy": "2021-12-15T20:25:17.640139Z",
     "iopub.status.idle": "2021-12-15T20:25:18.647340Z",
     "shell.execute_reply": "2021-12-15T20:25:18.646548Z"
    }
   },
   "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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21773</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14999</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22149</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>2 rows × 8 columns</p>"
      ],
      "text/plain": [
       "              Country  CustomerID  ... StockCode UnitPrice\n",
       "14998  United Kingdom     17419.0  ...     21773      1.25\n",
       "14999  United Kingdom     17419.0  ...     22149      2.10\n",
       "\n",
       "[2 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting columns\n",
    "\n",
    "you can also pass a list of columns to select columns from the data frame in a specified order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:18.654238Z",
     "iopub.status.busy": "2021-12-15T20:25:18.653517Z",
     "iopub.status.idle": "2021-12-15T20:25:19.431749Z",
     "shell.execute_reply": "2021-12-15T20:25:19.431127Z"
    }
   },
   "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",
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       "    }\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>Country</th>\n",
       "      <th>InvoiceDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 2 columns</p>"
      ],
      "text/plain": [
       "          Country          InvoiceDate\n",
       "0  United Kingdom  2010-12-01 08:26:00\n",
       "1  United Kingdom  2010-12-01 08:26:00\n",
       "2  United Kingdom  2010-12-01 08:26:00\n",
       "3  United Kingdom  2010-12-01 08:26:00\n",
       "4  United Kingdom  2010-12-01 08:26:00\n",
       "\n",
       "[5 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Country', 'InvoiceDate']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean Indexing\n",
    "\n",
    "we also allow you to filter the data frame using boolean indexing. Under the hood, a boolean index maps to a `terms` query that is then passed to opensearch to filter the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:19.440640Z",
     "iopub.status.busy": "2021-12-15T20:25:19.439831Z",
     "iopub.status.idle": "2021-12-15T20:25:20.066747Z",
     "shell.execute_reply": "2021-12-15T20:25:20.067477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'term': {'Country': 'Germany'}}\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22809</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84347</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84945</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22242</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22244</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 8 columns</p>"
      ],
      "text/plain": [
       "      Country  CustomerID  ... StockCode UnitPrice\n",
       "1109  Germany     12662.0  ...     22809      2.95\n",
       "1110  Germany     12662.0  ...     84347      2.55\n",
       "1111  Germany     12662.0  ...     84945      0.85\n",
       "1112  Germany     12662.0  ...     22242      1.65\n",
       "1113  Germany     12662.0  ...     22244      1.95\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the construction of a boolean vector maps directly to an opensearch query\n",
    "print(df['Country']=='Germany')\n",
    "df[(df['Country']=='Germany')].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also filter the data frame using a list of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:20.077022Z",
     "iopub.status.busy": "2021-12-15T20:25:20.076412Z",
     "iopub.status.idle": "2021-12-15T20:25:21.233013Z",
     "shell.execute_reply": "2021-12-15T20:25:21.234073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'terms': {'Country': ['Germany', 'United States']}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85123A</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71053</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84406B</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84029G</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84029E</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 8 columns</p>"
      ],
      "text/plain": [
       "          Country  CustomerID  ... StockCode UnitPrice\n",
       "0  United Kingdom     17850.0  ...    85123A      2.55\n",
       "1  United Kingdom     17850.0  ...     71053      3.39\n",
       "2  United Kingdom     17850.0  ...    84406B      2.75\n",
       "3  United Kingdom     17850.0  ...    84029G      3.39\n",
       "4  United Kingdom     17850.0  ...    84029E      3.39\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df['Country'].isin(['Germany', 'United States']))\n",
    "df[df['Country'].isin(['Germany', 'United Kingdom'])].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also combine boolean vectors to further filter the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:21.245390Z",
     "iopub.status.busy": "2021-12-15T20:25:21.244737Z",
     "iopub.status.idle": "2021-12-15T20:25:22.358701Z",
     "shell.execute_reply": "2021-12-15T20:25:22.355150Z"
    }
   },
   "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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>0 rows × 8 columns</p>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, CustomerID, Description, InvoiceDate, InvoiceNo, Quantity, StockCode, UnitPrice]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Country']=='Germany') & (df['Quantity']>90)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this example, let see how opensearch_py_ml translates this boolean filter to an opensearch `bool` query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:22.383610Z",
     "iopub.status.busy": "2021-12-15T20:25:22.370577Z",
     "iopub.status.idle": "2021-12-15T20:25:22.390275Z",
     "shell.execute_reply": "2021-12-15T20:25:22.388963Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os_index_pattern: online-retail\n",
      "Index:\n",
      " os_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "            os_field_name  is_source os_dtype os_date_format pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_os_field_name\n",
      "Country           Country       True  keyword           None   object           True             True        False                    Country\n",
      "CustomerID     CustomerID       True   double           None  float64           True             True        False                 CustomerID\n",
      "Description   Description       True  keyword           None   object           True             True        False                Description\n",
      "InvoiceDate   InvoiceDate       True  keyword           None   object           True             True        False                InvoiceDate\n",
      "InvoiceNo       InvoiceNo       True  keyword           None   object           True             True        False                  InvoiceNo\n",
      "Quantity         Quantity       True     long           None    int64           True             True        False                   Quantity\n",
      "StockCode       StockCode       True  keyword           None   object           True             True        False                  StockCode\n",
      "UnitPrice       UnitPrice       True   double           None  float64           True             True        False                  UnitPrice\n",
      "Operations:\n",
      " tasks: [('boolean_filter': ('boolean_filter': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}))]\n",
      " size: None\n",
      " sort_params: None\n",
      " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n",
      " body: {'query': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}}\n",
      " post_processing: []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df[(df['Country']=='Germany') & (df['Quantity']>90)].os_info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation and Descriptive Statistics\n",
    "\n",
    "Let's begin to ask some questions of our data and use opensearch_py_ml to get the answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many different countries are there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:22.398231Z",
     "iopub.status.busy": "2021-12-15T20:25:22.397459Z",
     "iopub.status.idle": "2021-12-15T20:25:22.482238Z",
     "shell.execute_reply": "2021-12-15T20:25:22.481338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the total sum of products ordered?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:22.492668Z",
     "iopub.status.busy": "2021-12-15T20:25:22.491590Z",
     "iopub.status.idle": "2021-12-15T20:25:22.580015Z",
     "shell.execute_reply": "2021-12-15T20:25:22.578300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111960"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show me the sum, mean, min, and max of the qunatity and unit_price fields**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:22.601432Z",
     "iopub.status.busy": "2021-12-15T20:25:22.600117Z",
     "iopub.status.idle": "2021-12-15T20:25:22.702450Z",
     "shell.execute_reply": "2021-12-15T20:25:22.701499Z"
    }
   },
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>111960.000</td>\n",
       "      <td>61548.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.464</td>\n",
       "      <td>4.103233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2880.000</td>\n",
       "      <td>950.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9360.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Quantity     UnitPrice\n",
       "sum   111960.000  61548.490000\n",
       "mean       7.464      4.103233\n",
       "max     2880.000    950.990000\n",
       "min    -9360.000      0.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Quantity','UnitPrice']].agg(['sum', 'mean', 'max', 'min'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Give me descriptive statistics for the entire data frame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:22.712002Z",
     "iopub.status.busy": "2021-12-15T20:25:22.711114Z",
     "iopub.status.idle": "2021-12-15T20:25:22.982698Z",
     "shell.execute_reply": "2021-12-15T20:25:22.981770Z"
    }
   },
   "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>CustomerID</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10729.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15590.776680</td>\n",
       "      <td>7.464000</td>\n",
       "      <td>4.103233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1764.189592</td>\n",
       "      <td>85.930116</td>\n",
       "      <td>20.106214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12347.000000</td>\n",
       "      <td>-9360.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14222.689466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15668.019608</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17218.806604</td>\n",
       "      <td>6.472000</td>\n",
       "      <td>4.212788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18239.000000</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>950.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID      Quantity     UnitPrice\n",
       "count  10729.000000  15000.000000  15000.000000\n",
       "mean   15590.776680      7.464000      4.103233\n",
       "std     1764.189592     85.930116     20.106214\n",
       "min    12347.000000  -9360.000000      0.000000\n",
       "25%    14222.689466      1.000000      1.250000\n",
       "50%    15668.019608      2.000000      2.510000\n",
       "75%    17218.806604      6.472000      4.212788\n",
       "max    18239.000000   2880.000000    950.990000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show me a histogram of numeric columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:23.000466Z",
     "iopub.status.busy": "2021-12-15T20:25:22.999571Z",
     "iopub.status.idle": "2021-12-15T20:25:23.576387Z",
     "shell.execute_reply": "2021-12-15T20:25:23.576703Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['Quantity']>-50) & \n",
    "   (df['Quantity']<50) & \n",
    "   (df['UnitPrice']>0) & \n",
    "   (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:23.584264Z",
     "iopub.status.busy": "2021-12-15T20:25:23.583784Z",
     "iopub.status.idle": "2021-12-15T20:25:24.494000Z",
     "shell.execute_reply": "2021-12-15T20:25:24.493618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['Quantity']>-50) & \n",
    "   (df['Quantity']<50) & \n",
    "   (df['UnitPrice']>0) & \n",
    "   (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30, log=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:24.504460Z",
     "iopub.status.busy": "2021-12-15T20:25:24.504086Z",
     "iopub.status.idle": "2021-12-15T20:25:26.468550Z",
     "shell.execute_reply": "2021-12-15T20:25:26.466711Z"
    }
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   "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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>...</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13748.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22086</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15291.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21733</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>14688.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21212</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>14688.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85071B</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>16029.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85099C</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14784</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15061.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22423</td>\n",
       "      <td>10.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14785</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15061.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22075</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14788</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15061.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17038</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>14739.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21704</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>14739.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22178</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>258 rows × 8 columns</p>"
      ],
      "text/plain": [
       "              Country  CustomerID  ... StockCode UnitPrice\n",
       "46     United Kingdom     13748.0  ...     22086      2.55\n",
       "83     United Kingdom     15291.0  ...     21733      2.55\n",
       "96     United Kingdom     14688.0  ...     21212      0.42\n",
       "102    United Kingdom     14688.0  ...    85071B      0.38\n",
       "176    United Kingdom     16029.0  ...    85099C      1.65\n",
       "...               ...         ...  ...       ...       ...\n",
       "14784  United Kingdom     15061.0  ...     22423     10.95\n",
       "14785  United Kingdom     15061.0  ...     22075      1.45\n",
       "14788  United Kingdom     15061.0  ...     17038      0.07\n",
       "14974  United Kingdom     14739.0  ...     21704      0.72\n",
       "14980  United Kingdom     14739.0  ...     22178      1.06\n",
       "\n",
       "[258 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('Quantity>50 & UnitPrice<100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arithmetic Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:26.483774Z",
     "iopub.status.busy": "2021-12-15T20:25:26.482084Z",
     "iopub.status.idle": "2021-12-15T20:25:26.907406Z",
     "shell.execute_reply": "2021-12-15T20:25:26.906448Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    6\n",
       "2    8\n",
       "3    6\n",
       "4    6\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:26.912916Z",
     "iopub.status.busy": "2021-12-15T20:25:26.910149Z",
     "iopub.status.idle": "2021-12-15T20:25:27.361783Z",
     "shell.execute_reply": "2021-12-15T20:25:27.362723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.55\n",
       "1    3.39\n",
       "2    2.75\n",
       "3    3.39\n",
       "4    3.39\n",
       "Name: UnitPrice, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UnitPrice'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:27.383414Z",
     "iopub.status.busy": "2021-12-15T20:25:27.374098Z",
     "iopub.status.idle": "2021-12-15T20:25:27.387546Z",
     "shell.execute_reply": "2021-12-15T20:25:27.388753Z"
    }
   },
   "outputs": [],
   "source": [
    "product = df['Quantity'] * df['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:27.398754Z",
     "iopub.status.busy": "2021-12-15T20:25:27.397557Z",
     "iopub.status.idle": "2021-12-15T20:25:27.818022Z",
     "shell.execute_reply": "2021-12-15T20:25:27.819640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15.30\n",
       "1    20.34\n",
       "2    22.00\n",
       "3    20.34\n",
       "4    20.34\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "String concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T20:25:27.837007Z",
     "iopub.status.busy": "2021-12-15T20:25:27.836370Z",
     "iopub.status.idle": "2021-12-15T20:25:29.072872Z",
     "shell.execute_reply": "2021-12-15T20:25:29.074153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        United Kingdom85123A\n",
       "1         United Kingdom71053\n",
       "2        United Kingdom84406B\n",
       "3        United Kingdom84029G\n",
       "4        United Kingdom84029E\n",
       "                 ...         \n",
       "14995    United Kingdom72349B\n",
       "14996     United Kingdom72741\n",
       "14997     United Kingdom22762\n",
       "14998     United Kingdom21773\n",
       "14999     United Kingdom22149\n",
       "Length: 15000, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'] + df['StockCode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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