src-3/eland_ml_script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import eland official API\n",
    "# check it on https://github.com/elastic/eland\n",
    "import eland as ed\n",
    "\n",
    "# other imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import elasticsearch-py client\n",
    "from elasticsearch import Elasticsearch\n",
    "\n",
    "# Function for pretty-printing JSON\n",
    "def json(raw):\n",
    "    import json\n",
    "    print(json.dumps(raw, indent=2, sort_keys=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"cluster_name\": \"elasticsearch\",\n",
      "  \"cluster_uuid\": \"W6oNRPiLRW-6V7AzSXEkqQ\",\n",
      "  \"name\": \"DESKTOP-Q85BIOJ\",\n",
      "  \"tagline\": \"You Know, for Search\",\n",
      "  \"version\": {\n",
      "    \"build_date\": \"2020-08-17T19:33:01.471239Z\",\n",
      "    \"build_flavor\": \"default\",\n",
      "    \"build_hash\": \"1ed4091d9ea4066863943682f9e1458367a7a72b\",\n",
      "    \"build_snapshot\": true,\n",
      "    \"build_type\": \"zip\",\n",
      "    \"lucene_version\": \"8.5.1\",\n",
      "    \"minimum_index_compatibility_version\": \"6.0.0-beta1\",\n",
      "    \"minimum_wire_compatibility_version\": \"6.8.0\",\n",
      "    \"number\": \"7.8.2-SNAPSHOT\"\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Connect to an Elasticsearch instance\n",
    "# here we use the official Elastic Python client\n",
    "# check it on https://github.com/elastic/elasticsearch-py\n",
    "es = Elasticsearch(\n",
    "  ['http://localhost:9200'],\n",
    "  http_auth=(\"elastic\", \"changeme\")\n",
    ")\n",
    "# print the connection object info (same as visiting http://localhost:9200)\n",
    "# make sure your elasticsearch node/cluster respond to requests\n",
    "json(es.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>avg_revenue</th>\n",
       "      <th>count_orders</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>date</th>\n",
       "      <th>first_invoice</th>\n",
       "      <th>last_invoice</th>\n",
       "      <th>latest_invoice</th>\n",
       "      <th>quantity</th>\n",
       "      <th>recency</th>\n",
       "      <th>score</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA</th>\n",
       "      <td>242.564286</td>\n",
       "      <td>42.0</td>\n",
       "      <td>12347</td>\n",
       "      <td>1545264000000</td>\n",
       "      <td>2019-04-07 10:43:00+00:00</td>\n",
       "      <td>2019-06-09 13:01:00+00:00</td>\n",
       "      <td>2019-06-30 20:08:00+00:00</td>\n",
       "      <td>679.0</td>\n",
       "      <td>21</td>\n",
       "      <td>3416.900004</td>\n",
       "      <td>10187.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA</th>\n",
       "      <td>734.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12348</td>\n",
       "      <td>1545264000000</td>\n",
       "      <td>2019-04-05 10:47:00+00:00</td>\n",
       "      <td>2019-04-05 10:47:00+00:00</td>\n",
       "      <td>2019-06-30 20:08:00+00:00</td>\n",
       "      <td>269.0</td>\n",
       "      <td>86</td>\n",
       "      <td>1253.666667</td>\n",
       "      <td>3670.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA</th>\n",
       "      <td>222.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12353</td>\n",
       "      <td>1545264000000</td>\n",
       "      <td>2019-05-19 17:47:00+00:00</td>\n",
       "      <td>2019-05-19 17:47:00+00:00</td>\n",
       "      <td>2019-06-30 20:08:00+00:00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA</th>\n",
       "      <td>186.103448</td>\n",
       "      <td>58.0</td>\n",
       "      <td>12354</td>\n",
       "      <td>1545264000000</td>\n",
       "      <td>2019-04-21 13:11:00+00:00</td>\n",
       "      <td>2019-04-21 13:11:00+00:00</td>\n",
       "      <td>2019-06-30 20:08:00+00:00</td>\n",
       "      <td>530.0</td>\n",
       "      <td>70</td>\n",
       "      <td>3640.666662</td>\n",
       "      <td>10793.999985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA</th>\n",
       "      <td>353.384615</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12355</td>\n",
       "      <td>1545264000000</td>\n",
       "      <td>2019-05-09 13:49:00+00:00</td>\n",
       "      <td>2019-05-09 13:49:00+00:00</td>\n",
       "      <td>2019-06-30 20:08:00+00:00</td>\n",
       "      <td>240.0</td>\n",
       "      <td>52</td>\n",
       "      <td>1553.000000</td>\n",
       "      <td>4594.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 11 columns</p>"
      ],
      "text/plain": [
       "                                  avg_revenue  count_orders customer_id  \\\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA   242.564286          42.0       12347   \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA   734.000000           5.0       12348   \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA   222.500000           4.0       12353   \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA   186.103448          58.0       12354   \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA   353.384615          13.0       12355   \n",
       "\n",
       "                                           date             first_invoice  \\\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA  1545264000000 2019-04-07 10:43:00+00:00   \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA  1545264000000 2019-04-05 10:47:00+00:00   \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA  1545264000000 2019-05-19 17:47:00+00:00   \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA  1545264000000 2019-04-21 13:11:00+00:00   \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA  1545264000000 2019-05-09 13:49:00+00:00   \n",
       "\n",
       "                                              last_invoice  \\\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA 2019-06-09 13:01:00+00:00   \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA 2019-04-05 10:47:00+00:00   \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA 2019-05-19 17:47:00+00:00   \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA 2019-04-21 13:11:00+00:00   \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA 2019-05-09 13:49:00+00:00   \n",
       "\n",
       "                                            latest_invoice  quantity  recency  \\\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA 2019-06-30 20:08:00+00:00     679.0       21   \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA 2019-06-30 20:08:00+00:00     269.0       86   \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA 2019-06-30 20:08:00+00:00      20.0       42   \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA 2019-06-30 20:08:00+00:00     530.0       70   \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA 2019-06-30 20:08:00+00:00     240.0       52   \n",
       "\n",
       "                                        score  total_revenue  \n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA  3416.900004   10187.700012  \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA  1253.666667    3670.000000  \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA   312.000000     890.000000  \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA  3640.666662   10793.999985  \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA  1553.000000    4594.000000  \n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_invoices = ed.DataFrame(es, 'es-customers-3m')\n",
    "ed_invoices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>count_orders</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA</th>\n",
       "      <td>12347</td>\n",
       "      <td>21</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10187.700012</td>\n",
       "      <td>3416.900004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA</th>\n",
       "      <td>12348</td>\n",
       "      <td>86</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3670.000000</td>\n",
       "      <td>1253.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA</th>\n",
       "      <td>12353</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA</th>\n",
       "      <td>12354</td>\n",
       "      <td>70</td>\n",
       "      <td>58.0</td>\n",
       "      <td>10793.999985</td>\n",
       "      <td>3640.666662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA</th>\n",
       "      <td>12355</td>\n",
       "      <td>52</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4594.000000</td>\n",
       "      <td>1553.000000</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>Mea8Bl9aZnsbIV5xrhGJLwIAAAAAAAAA</th>\n",
       "      <td>18270</td>\n",
       "      <td>78</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-442.000000</td>\n",
       "      <td>-120.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MY9c91igA-wqYwslQknPYTwAAAAAAAAA</th>\n",
       "      <td>18272</td>\n",
       "      <td>50</td>\n",
       "      <td>59.0</td>\n",
       "      <td>9667.399994</td>\n",
       "      <td>3258.799998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSyPXhIl0iUGsMapytzeKt8AAAAAAAAA</th>\n",
       "      <td>18281</td>\n",
       "      <td>18</td>\n",
       "      <td>7.0</td>\n",
       "      <td>808.200005</td>\n",
       "      <td>277.733335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZOaY3B5537BlEZDYvjxIbAAAAAAAAAA</th>\n",
       "      <td>18283</td>\n",
       "      <td>7</td>\n",
       "      <td>211.0</td>\n",
       "      <td>5246.799967</td>\n",
       "      <td>1821.599989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUA1YxSdF5f703BNFJQv2F8AAAAAAAAA</th>\n",
       "      <td>18287</td>\n",
       "      <td>39</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7652.800003</td>\n",
       "      <td>2573.600001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2153 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 customer_id  recency  count_orders  \\\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA       12347       21          42.0   \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA       12348       86           5.0   \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA       12353       42           4.0   \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA       12354       70          58.0   \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA       12355       52          13.0   \n",
       "...                                      ...      ...           ...   \n",
       "Mea8Bl9aZnsbIV5xrhGJLwIAAAAAAAAA       18270       78           2.0   \n",
       "MY9c91igA-wqYwslQknPYTwAAAAAAAAA       18272       50          59.0   \n",
       "MSyPXhIl0iUGsMapytzeKt8AAAAAAAAA       18281       18           7.0   \n",
       "MZOaY3B5537BlEZDYvjxIbAAAAAAAAAA       18283        7         211.0   \n",
       "MUA1YxSdF5f703BNFJQv2F8AAAAAAAAA       18287       39          29.0   \n",
       "\n",
       "                                  total_revenue        score  \n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA   10187.700012  3416.900004  \n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA    3670.000000  1253.666667  \n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA     890.000000   312.000000  \n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA   10793.999985  3640.666662  \n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA    4594.000000  1553.000000  \n",
       "...                                         ...          ...  \n",
       "Mea8Bl9aZnsbIV5xrhGJLwIAAAAAAAAA    -442.000000  -120.666667  \n",
       "MY9c91igA-wqYwslQknPYTwAAAAAAAAA    9667.399994  3258.799998  \n",
       "MSyPXhIl0iUGsMapytzeKt8AAAAAAAAA     808.200005   277.733335  \n",
       "MZOaY3B5537BlEZDYvjxIbAAAAAAAAAA    5246.799967  1821.599989  \n",
       "MUA1YxSdF5f703BNFJQv2F8AAAAAAAAA    7652.800003  2573.600001  \n",
       "\n",
       "[2153 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_users = ed.eland_to_pandas(ed_invoices.filter(['customer_id','recency','count_orders','total_revenue','score']))\n",
    "ed_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed_users['recency'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWYElEQVR4nO3df5DcdX3H8eeriUTk+BEMXGNCmzATmOaHjeYGcajMRrREZAQ6tU2GERCcKEJHp8xUUpxK62SGqvEHoOApKTBEjhTEUIRqZLhCpyAmGEgCRC7kqkcySTEYOGUyJr77x37ObO72bpPd2292/bweMzv73ff3+/l+3/u95HV7n/3unSICMzPLwx8d6QbMzKw4Dn0zs4w49M3MMuLQNzPLiEPfzCwjE490A7VMmTIlZsyYUff4X//61xxzzDHj11CTtEuf4F6boV36BPfaDM3oc/369a9ExEkjVkRES98WLFgQjXj00UcbGl+Udukzwr02Q7v0GeFem6EZfQLrokqmenrHzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjLf9rGHKx8eU9XHbt90fU+2/44BHoxsz+UPmVvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRmqGvqSVknZJ2lRRu0fShnTrl7Qh1WdIeqNi3a0VYxZI2iipT9KNktSUZ2RmZqM6lEs2bwduBu4cKkTE3w4tS1oB7KnYfmtEzK+yn1uApcCTwEPAIuDhw+7YzMzqVvOVfkQ8Buyuti69Wv8b4O6x9iFpKnBcRDyR/qLLncCFh92tmZk1ROUMrrGRNAN4MCLmDqufDXw5IroqttsM/Ax4DfhsRDwuqQu4ISLel7Z7D/CZiDh/lOMtpfxTAZ2dnQt6enrqe3bA4OAgHR0ddY8vyq7de9j5xsj6vGnHF99MDe1yTqF9em2XPsG9NkMz+ly4cOH6oWyu1Ogncpdw8Kv8HcCfRMQvJS0AvidpDlBt/n7U7zYR0Q10A3R1dUWpVKq7wd7eXhoZX5SbVq1hxcaRX47+i0vFN1NDu5xTaJ9e26VPcK/NUGSfdYe+pInAXwELhmoRsRfYm5bXS9oKnAYMANMrhk8Httd7bDMzq08jl2y+D3ghIgaGCpJOkjQhLZ8KzAJeiogdwOuSzkzvA1wCrGng2GZmVodDuWTzbuAJ4HRJA5KuSKsWM/IN3LOBZyU9A9wLfCIiht4EvhL4NtAHbMVX7piZFa7m9E5ELBmlflmV2n3AfaNsvw6YW22dmZkVw5/INTPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4wcyh9GXylpl6RNFbXrJb0saUO6nVexbpmkPklbJJ1bUV8gaWNad6Mkjf/TMTOzsRzKK/3bgUVV6l+JiPnp9hCApNnAYmBOGvMNSRPS9rcAS4FZ6VZtn2Zm1kQ1Qz8iHgN2H+L+LgB6ImJvRGwD+oAzJE0FjouIJyIigDuBC+vs2czM6jSxgbFXS7oEWAdcExGvAtOAJyu2GUi136bl4fWqJC2l/FMBnZ2d9Pb21t3k4OBgQ+OL0nk0XDNv34h6K/beLucU2qfXdukT3GszFNlnvaF/C/B5INL9CuByoNo8fYxRryoiuoFugK6uriiVSnW2WQ7NRsYX5aZVa1ixceSXo//iUvHN1NAu5xTap9d26RPcazMU2WddV+9ExM6I2B8RvwO+BZyRVg0Ap1RsOh3YnurTq9TNzKxAdYV+mqMfchEwdGXPA8BiSZMkzaT8hu1TEbEDeF3SmemqnUuANQ30bWZmdag5vSPpbqAETJE0AHwOKEmaT3mKph/4OEBEbJa0GngO2AdcFRH7066upHwl0NHAw+lmZmYFqhn6EbGkSvm2MbZfDiyvUl8HzD2s7szMbFz5E7lmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWkZqhL2mlpF2SNlXUvijpBUnPSrpf0gmpPkPSG5I2pNutFWMWSNooqU/SjZLUlGdkZmajOpRX+rcDi4bV1gJzI+LtwM+AZRXrtkbE/HT7REX9FmApMCvdhu/TzMyarGboR8RjwO5htR9GxL708Elg+lj7kDQVOC4inoiIAO4ELqyrYzMzq5vKGVxjI2kG8GBEzK2y7j+AeyLirrTdZsqv/l8DPhsRj0vqAm6IiPelMe8BPhMR549yvKWUfyqgs7NzQU9PTz3PDYDBwUE6OjrqHl+UXbv3sPONkfV5044vvpka2uWcQvv02i59gntthmb0uXDhwvUR0TW8PrGRnUq6DtgHrEqlHcCfRMQvJS0AvidpDlBt/n7U7zYR0Q10A3R1dUWpVKq7x97eXhoZX5SbVq1hxcaRX47+i0vFN1NDu5xTaJ9e26VPcK/NUGSfdYe+pEuB84Fz0pQNEbEX2JuW10vaCpwGDHDwFNB0YHu9xzYzs/rUdcmmpEXAZ4APRcRvKuonSZqQlk+l/IbtSxGxA3hd0pnpqp1LgDUNd29mZoel5it9SXcDJWCKpAHgc5Sv1pkErE1XXj6ZrtQ5G/gXSfuA/cAnImLoTeArKV8JdDTwcLqZmVmBaoZ+RCypUr5tlG3vA+4bZd06YMQbwWZmVhx/ItfMLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwyUjP0Ja2UtEvSporaiZLWSnox3U+uWLdMUp+kLZLOragvkLQxrbtR6S+qm5lZcQ7llf7twKJhtWuBRyJiFvBIeoyk2cBiYE4a8w1JE9KYW4ClwKx0G75PMzNrspqhHxGPAbuHlS8A7kjLdwAXVtR7ImJvRGwD+oAzJE0FjouIJyIigDsrxpiZWUHqndPvjIgdAOn+5FSfBvyiYruBVJuWlofXzcysQBPHeX/V5uljjHr1nUhLKU8F0dnZSW9vb90NDQ4ONjS+KJ1HwzXz9o2ot2Lv7XJOoX16bZc+wb02Q5F91hv6OyVNjYgdaepmV6oPAKdUbDcd2J7q06vUq4qIbqAboKurK0qlUp1tlkOzkfFFuWnVGlZsHPnl6L+4VHwzNbTLOYX26bVd+gT32gxF9lnv9M4DwKVp+VJgTUV9saRJkmZSfsP2qTQF9LqkM9NVO5dUjDEzs4LUfKUv6W6gBEyRNAB8DrgBWC3pCuDnwIcBImKzpNXAc8A+4KqI2J92dSXlK4GOBh5ONzMzK1DN0I+IJaOsOmeU7ZcDy6vU1wFzD6s7MzMbV/5ErplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlpG6Q1/S6ZI2VNxek/RpSddLermifl7FmGWS+iRtkXTu+DwFMzM7VBPrHRgRW4D5AJImAC8D9wMfBb4SEV+q3F7SbGAxMAd4G/AjSadFxP56ezAzs8MzXtM75wBbI+J/x9jmAqAnIvZGxDagDzhjnI5vZmaHQBHR+E6klcDTEXGzpOuBy4DXgHXANRHxqqSbgScj4q405jbg4Yi4t8r+lgJLATo7Oxf09PTU3dvg4CAdHR11jy/Krt172PnGyPq8accX30wN7XJOoX16bZc+wb02QzP6XLhw4fqI6Bper3t6Z4iko4APActS6Rbg80Ck+xXA5YCqDK/6HSciuoFugK6uriiVSnX319vbSyPji3LTqjWs2Djyy9F/can4Zmpol3MK7dNru/QJ7rUZiuxzPKZ3PkD5Vf5OgIjYGRH7I+J3wLc4MIUzAJxSMW46sH0cjm9mZodoPEJ/CXD30ANJUyvWXQRsSssPAIslTZI0E5gFPDUOxzczs0PU0PSOpLcA7wc+XlH+gqT5lKdu+ofWRcRmSauB54B9wFW+csfMrFgNhX5E/AZ467DaR8bYfjmwvJFjmplZ/fyJXDOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8tIQ6EvqV/SRkkbJK1LtRMlrZX0YrqfXLH9Mkl9krZIOrfR5s3M7PCMxyv9hRExPyK60uNrgUciYhbwSHqMpNnAYmAOsAj4hqQJ43B8MzM7RM2Y3rkAuCMt3wFcWFHviYi9EbEN6APOaMLxzcxsFIqI+gdL24BXgQC+GRHdkn4VESdUbPNqREyWdDPwZETcleq3AQ9HxL1V9rsUWArQ2dm5oKenp+4eBwcH6ejoqHt8UXbt3sPON0bW5007vvhmamiXcwrt02u79AnutRma0efChQvXV8zA/N7EBvd7VkRsl3QysFbSC2Nsqyq1qt9xIqIb6Abo6uqKUqlUd4O9vb00Mr4oN61aw4qNI78c/ReXim+mhnY5p9A+vbZLn+Bem6HIPhua3omI7el+F3A/5emanZKmAqT7XWnzAeCUiuHTge2NHN/MzA5P3aEv6RhJxw4tA38JbAIeAC5Nm10KrEnLDwCLJU2SNBOYBTxV7/HNzOzwNTK90wncL2loP9+JiP+U9BNgtaQrgJ8DHwaIiM2SVgPPAfuAqyJif0Pdm5nZYak79CPiJeDPq9R/CZwzypjlwPJ6j2lmZo3xJ3LNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsI3WHvqRTJD0q6XlJmyV9KtWvl/SypA3pdl7FmGWS+iRtkXTueDwBMzM7dHX/YXRgH3BNRDwt6VhgvaS1ad1XIuJLlRtLmg0sBuYAbwN+JOm0iNjfQA9mZnYY6n6lHxE7IuLptPw68DwwbYwhFwA9EbE3IrYBfcAZ9R7fzMwO37jM6UuaAbwD+HEqXS3pWUkrJU1OtWnALyqGDTD2NwkzMxtniojGdiB1AP8FLI+I70rqBF4BAvg8MDUiLpf0deCJiLgrjbsNeCgi7quyz6XAUoDOzs4FPT09dfc3ODhIR0dH3eOLsmv3Hna+MbI+b9rxxTdTQ7ucU2ifXtulT3CvzdCMPhcuXLg+IrqG1xuZ00fSm4D7gFUR8V2AiNhZsf5bwIPp4QBwSsXw6cD2avuNiG6gG6CrqytKpVLdPfb29tLI+KLctGoNKzaO/HL0X1wqvpka2uWcQvv02i59gntthiL7bOTqHQG3Ac9HxJcr6lMrNrsI2JSWHwAWS5okaSYwC3iq3uObmdnha+SV/lnAR4CNkjak2j8CSyTNpzy90w98HCAiNktaDTxH+cqfq3zlTm0zrv1+1Xr/DR8suBMz+0NQd+hHxH8DqrLqoTHGLAeW13tMMzNrjD+Ra2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGGvkbuXYE+W/nmlk9/ErfzCwjhb/Sl7QI+BowAfh2RNxQdA9/yPwTgJmNpdBX+pImAF8HPgDMBpZIml1kD2ZmOSv6lf4ZQF9EvAQgqQe4AHiu4D6OiNFehQNcM+/IHbua0X4ymHHt97lm3j4uO8T9+ScMs9aiiCjuYNJfA4si4mPp8UeAd0XE1cO2WwosTQ9PB7Y0cNgpwCsNjC9Ku/QJ7rUZ2qVPcK/N0Iw+/zQiThpeLPqVvqrURnzXiYhuoHtcDiiti4iu8dhXM7VLn+Bem6Fd+gT32gxF9ln01TsDwCkVj6cD2wvuwcwsW0WH/k+AWZJmSjoKWAw8UHAPZmbZKnR6JyL2Sboa+AHlSzZXRsTmJh92XKaJCtAufYJ7bYZ26RPcazMU1mehb+SamdmR5U/kmpllxKFvZpaTiGjpG/BhYDPwO6Br2LplQB/l6/jPragvADamdTdyYBprEnBPqv8YmFEx5lLgxXS7tKI+M237Yhp7VBOe46L0HPqAa5t4LlcCu4BNFbUTgbXp+a0FJhd5fkfp8xTgUeD59LX/VAv3+mbgKeCZ1Os/t2qvafsJwE+BB1u8z/50jA3Auhbv9QTgXuAFyv9m392qvUZEW4T+n1H+gFYvFaFP+dc4PJNOyExgKzAhrXsqnXgBDwMfSPVPArem5cXAPRX/mF5K95PT8uS0bjWwOC3fClw5zs9vQur9VOCo9JxmN+lcng28k4ND/wukbzTAtcC/Fnl+R+lzKvDOtHws8LPUTyv2KqAjLb+J8n/KM1ux1zTm74HvcCD0W7XPfmDKsFqr9noH8LG0fBTlbwIt2WtEG4R+xYnt5eDQXwYsq3j8g3TCpgIvVNSXAN+s3CYtT6T8CThVbpPWfTPVlLaZmOrvBn4wzs/roH0Of15NOI8zODj0twBT0/JUYEtR5/cwel4DvL/VewXeAjwNvKsVe6X8uZhHgPdyIPRbrs+0TT8jQ7/legWOA7aRXpW3cq9Dt3ae058G/KLi8UCqTUvLw+sHjYmIfcAe4K1j7OutwK/StsP3NV5GO3ZROiNiB0C6P7lGX+N5fmuSNAN4B+VX0C3Zq6QJkjZQnjpbGxGt2utXgX+gPFU6pBX7hPIn9X8oaX36tSyt2uupwP8B/ybpp5K+LemYFu0VaJE/oiLpR8AfV1l1XUSsGW1YlVqMUa9nzCH92ogGFXGMehRxfsduQOoA7gM+HRGvSdV2U/dxx63XiNgPzJd0AnC/pLljbH5EepV0PrArItZLKo3R3++H1HHM8fz6nxUR2yWdDKyV9MIY2x7JXidSnjL9u4j4saSvUZ7OacVegRa5eici3hcRc6vcRgt8GP1XOgyk5eH1g8ZImggcD+weY1+vACekbYfva7wc6V9NsVPSVIB0v6tGX+N5fkcl6U2UA39VRHy3lXsdEhG/ojwNuagFez0L+JCkfqAHeK+ku1qwTwAiYnu63wXcT/k39LZirwPAQPrpDspv6L6zRXstO5S5yla4MXJOfw4HvyHyEgfeEPkJ5TfTht4QOS/Vr+LgN0RWp+UTKc/LTU63bcCJad2/c/AbuZ8c5+c1MfU+kwNv5M5p4nmcwcFz+l/k4DecvlDk+R2lRwF3Al8dVm/FXk8CTkjLRwOPA+e3Yq8VPZc4MKffcn0CxwDHViz/D+VvpC3XaxrzOHB6Wr4+9dmSvUZE64c+cBHl72h7gZ0c/KbndZTf/d5Ceqc71buATWndzRy49OnNlEO8j/I75adWjLk81fuAj1bUT03b9qWxk5rwHM+jfIXKVspTWs06l3cDO4DfpnN6BeW5wUcoX/L1SOU/miLO7yh9/gXlH1OfpXzJ3oZ0jlqx17dTvgTy2XScf0r1luu1YkyJA6Hfcn1S/j/3DAcug72uVXtN288H1qV/A9+jHMAt2WtE+NcwmJnlpCXm9M3MrBgOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy8v+z55uJVHFIYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed_users['total_revenue'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed_users['count_orders'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>customer_id</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA</th>\n",
       "      <td>12347</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA</th>\n",
       "      <td>12348</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA</th>\n",
       "      <td>12353</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA</th>\n",
       "      <td>12354</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA</th>\n",
       "      <td>12355</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mea8Bl9aZnsbIV5xrhGJLwIAAAAAAAAA</th>\n",
       "      <td>18270</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MY9c91igA-wqYwslQknPYTwAAAAAAAAA</th>\n",
       "      <td>18272</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSyPXhIl0iUGsMapytzeKt8AAAAAAAAA</th>\n",
       "      <td>18281</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZOaY3B5537BlEZDYvjxIbAAAAAAAAAA</th>\n",
       "      <td>18283</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUA1YxSdF5f703BNFJQv2F8AAAAAAAAA</th>\n",
       "      <td>18287</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2153 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 customer_id  recency\n",
       "MawpT5q-8qYGhSR1nOT0lVUAAAAAAAAA       12347       21\n",
       "MejNds7kf1BrMX1GJw2hSdoAAAAAAAAA       12348       86\n",
       "MQjrtyn9qWwyhrc9x8Tvp4UAAAAAAAAA       12353       42\n",
       "MfnqaxUOan2qLH8Z-ASY_vQAAAAAAAAA       12354       70\n",
       "MWKN15sMpN24kbxW5rTqFsYAAAAAAAAA       12355       52\n",
       "...                                      ...      ...\n",
       "Mea8Bl9aZnsbIV5xrhGJLwIAAAAAAAAA       18270       78\n",
       "MY9c91igA-wqYwslQknPYTwAAAAAAAAA       18272       50\n",
       "MSyPXhIl0iUGsMapytzeKt8AAAAAAAAA       18281       18\n",
       "MZOaY3B5537BlEZDYvjxIbAAAAAAAAAA       18283        7\n",
       "MUA1YxSdF5f703BNFJQv2F8AAAAAAAAA       18287       39\n",
       "\n",
       "[2153 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recency = ed_users.filter(['customer_id','recency'])\n",
    "recency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "sse={}\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(recency)\n",
    "    sse[k] = kmeans.inertia_ \n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of cluster\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecencyCluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>528.0</td>\n",
       "      <td>33.791667</td>\n",
       "      <td>25.480546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.25</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628.0</td>\n",
       "      <td>33.786624</td>\n",
       "      <td>24.940868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489.0</td>\n",
       "      <td>34.392638</td>\n",
       "      <td>24.849902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>508.0</td>\n",
       "      <td>34.779528</td>\n",
       "      <td>25.647178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std  min   25%   50%    75%   max\n",
       "RecencyCluster                                                           \n",
       "0               528.0  33.791667  25.480546  0.0  13.0  28.0  51.25  90.0\n",
       "1               628.0  33.786624  24.940868  0.0  11.0  29.0  51.00  90.0\n",
       "2               489.0  34.392638  24.849902  0.0  14.0  29.0  51.00  90.0\n",
       "3               508.0  34.779528  25.647178  0.0  14.0  28.0  52.00  90.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build 4 clusters for recency and add it to dataframe\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(recency)\n",
    "ed_users['RecencyCluster'] = kmeans.predict(recency)\n",
    "ed_users.groupby('RecencyCluster')['recency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecencyCluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>508.0</td>\n",
       "      <td>34.779528</td>\n",
       "      <td>25.647178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489.0</td>\n",
       "      <td>34.392638</td>\n",
       "      <td>24.849902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>528.0</td>\n",
       "      <td>33.791667</td>\n",
       "      <td>25.480546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.25</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628.0</td>\n",
       "      <td>33.786624</td>\n",
       "      <td>24.940868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std  min   25%   50%    75%   max\n",
       "RecencyCluster                                                           \n",
       "0               508.0  34.779528  25.647178  0.0  14.0  28.0  52.00  90.0\n",
       "1               489.0  34.392638  24.849902  0.0  14.0  29.0  51.00  90.0\n",
       "2               528.0  33.791667  25.480546  0.0  13.0  28.0  51.25  90.0\n",
       "3               628.0  33.786624  24.940868  0.0  11.0  29.0  51.00  90.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#function for ordering cluster numbers\n",
    "def order_cluster(cluster_field_name, target_field_name,df,ascending):\n",
    "    new_cluster_field_name = 'new_' + cluster_field_name\n",
    "    df_new = df.groupby(cluster_field_name)[target_field_name].mean().reset_index()\n",
    "    df_new = df_new.sort_values(by=target_field_name,ascending=ascending).reset_index(drop=True)\n",
    "    df_new['index'] = df_new.index\n",
    "    df_final = pd.merge(df,df_new[[cluster_field_name,'index']], on=cluster_field_name)\n",
    "    df_final = df_final.drop([cluster_field_name],axis=1)\n",
    "    df_final = df_final.rename(columns={\"index\":cluster_field_name})\n",
    "    return df_final\n",
    "\n",
    "ed_users = order_cluster('RecencyCluster', 'recency',ed_users,False)\n",
    "ed_users.groupby('RecencyCluster')['recency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyCluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>491.0</td>\n",
       "      <td>35.095723</td>\n",
       "      <td>38.068373</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>45.50</td>\n",
       "      <td>291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>508.0</td>\n",
       "      <td>39.059055</td>\n",
       "      <td>53.104679</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>633.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>528.0</td>\n",
       "      <td>43.837121</td>\n",
       "      <td>76.717649</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1067.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>626.0</td>\n",
       "      <td>69.648562</td>\n",
       "      <td>132.099349</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>69.75</td>\n",
       "      <td>1686.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean         std  min   25%   50%    75%     max\n",
       "FrequencyCluster                                                              \n",
       "0                 491.0  35.095723   38.068373  1.0  12.0  22.0  45.50   291.0\n",
       "1                 508.0  39.059055   53.104679  1.0  12.0  25.0  49.00   633.0\n",
       "2                 528.0  43.837121   76.717649  1.0  13.0  25.0  48.00  1067.0\n",
       "3                 626.0  69.648562  132.099349  1.0  14.0  30.0  69.75  1686.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency = ed_users.filter(['customer_id','count_orders'])\n",
    "\n",
    "#k-means\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(frequency)\n",
    "ed_users['FrequencyCluster'] = kmeans.predict(frequency)\n",
    "\n",
    "#order the frequency cluster\n",
    "ed_users = order_cluster('FrequencyCluster', 'count_orders',ed_users,True)\n",
    "\n",
    "#see details of each cluster\n",
    "ed_users.groupby('FrequencyCluster')['count_orders'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevenueCluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2075.0</td>\n",
       "      <td>5821.584393</td>\n",
       "      <td>6243.996931</td>\n",
       "      <td>-101531.499222</td>\n",
       "      <td>2110.199995</td>\n",
       "      <td>3924.000000</td>\n",
       "      <td>7662.999983</td>\n",
       "      <td>32460.909990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.0</td>\n",
       "      <td>59524.492865</td>\n",
       "      <td>24025.731502</td>\n",
       "      <td>32968.000008</td>\n",
       "      <td>42745.000002</td>\n",
       "      <td>52074.500140</td>\n",
       "      <td>70081.800060</td>\n",
       "      <td>135229.999821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>244469.100145</td>\n",
       "      <td>78928.417639</td>\n",
       "      <td>168141.699709</td>\n",
       "      <td>177432.849933</td>\n",
       "      <td>239865.900133</td>\n",
       "      <td>293990.350446</td>\n",
       "      <td>360429.700417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>589188.100760</td>\n",
       "      <td>31750.791043</td>\n",
       "      <td>566736.901106</td>\n",
       "      <td>577962.500933</td>\n",
       "      <td>589188.100760</td>\n",
       "      <td>600413.700588</td>\n",
       "      <td>611639.300415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count           mean           std            min  \\\n",
       "RevenueCluster                                                       \n",
       "0               2075.0    5821.584393   6243.996931 -101531.499222   \n",
       "1                 69.0   59524.492865  24025.731502   32968.000008   \n",
       "2                  7.0  244469.100145  78928.417639  168141.699709   \n",
       "3                  2.0  589188.100760  31750.791043  566736.901106   \n",
       "\n",
       "                          25%            50%            75%            max  \n",
       "RevenueCluster                                                              \n",
       "0                 2110.199995    3924.000000    7662.999983   32460.909990  \n",
       "1                42745.000002   52074.500140   70081.800060  135229.999821  \n",
       "2               177432.849933  239865.900133  293990.350446  360429.700417  \n",
       "3               577962.500933  589188.100760  600413.700588  611639.300415  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_revenue = ed_users.filter(['customer_id','total_revenue'])\n",
    "\n",
    "#k-means\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(total_revenue)\n",
    "ed_users['RevenueCluster'] = kmeans.predict(total_revenue)\n",
    "\n",
    "#order the revenue cluster\n",
    "ed_users = order_cluster('RevenueCluster', 'total_revenue',ed_users,True)\n",
    "\n",
    "#see details of each cluster\n",
    "ed_users.groupby('RevenueCluster')['total_revenue'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-65-55d80504c653>:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  score = ed_users.groupby('OverallScore')['recency','count_orders','total_revenue'].mean()\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",
       "        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>recency</th>\n",
       "      <th>count_orders</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallScore</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.253870</td>\n",
       "      <td>34.372549</td>\n",
       "      <td>5980.725286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.481481</td>\n",
       "      <td>130.185185</td>\n",
       "      <td>63918.240797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.333333</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>121604.366803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.518447</td>\n",
       "      <td>37.699029</td>\n",
       "      <td>5725.517688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>113.875000</td>\n",
       "      <td>53125.275019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.586847</td>\n",
       "      <td>51.126476</td>\n",
       "      <td>6904.406583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.542857</td>\n",
       "      <td>445.428571</td>\n",
       "      <td>72089.491632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>210.500000</td>\n",
       "      <td>293990.350446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>611639.300415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recency  count_orders  total_revenue\n",
       "OverallScore                                        \n",
       "1             35.253870     34.372549    5980.725286\n",
       "2             11.481481    130.185185   63918.240797\n",
       "3             30.333333     84.000000  121604.366803\n",
       "4             34.518447     37.699029    5725.517688\n",
       "5              7.250000    113.875000   53125.275019\n",
       "6             34.586847     51.126476    6904.406583\n",
       "7             17.542857    445.428571   72089.491632\n",
       "8              4.500000    210.500000  293990.350446\n",
       "9             16.000000     91.000000  611639.300415"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate overall score and use mean() to see details\n",
    "ed_users['OverallScore'] = ed_users['RecencyCluster'] + ed_users['FrequencyCluster'] + ed_users['RevenueCluster']\n",
    "score = ed_users.groupby('OverallScore')['recency','count_orders','total_revenue'].mean()\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>OverallScore</th>\n",
       "      <th>count_orders</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>12347</td>\n",
       "      <td>21</td>\n",
       "      <td>10187.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12348</td>\n",
       "      <td>86</td>\n",
       "      <td>3670.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12353</td>\n",
       "      <td>42</td>\n",
       "      <td>890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>12354</td>\n",
       "      <td>70</td>\n",
       "      <td>10793.999985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12355</td>\n",
       "      <td>52</td>\n",
       "      <td>4594.000000</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>2148</th>\n",
       "      <td>6</td>\n",
       "      <td>505.0</td>\n",
       "      <td>15311</td>\n",
       "      <td>1</td>\n",
       "      <td>173184.199470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>8</td>\n",
       "      <td>392.0</td>\n",
       "      <td>17450</td>\n",
       "      <td>9</td>\n",
       "      <td>339573.900843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17949</td>\n",
       "      <td>0</td>\n",
       "      <td>248406.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>7</td>\n",
       "      <td>437.0</td>\n",
       "      <td>14646</td>\n",
       "      <td>2</td>\n",
       "      <td>566736.901106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>9</td>\n",
       "      <td>91.0</td>\n",
       "      <td>18102</td>\n",
       "      <td>16</td>\n",
       "      <td>611639.300415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>2153 rows × 5 columns</p>"
      ],
      "text/plain": [
       "      OverallScore  count_orders customer_id  recency  total_revenue\n",
       "0                1          42.0       12347       21   10187.700012\n",
       "1                1           5.0       12348       86    3670.000000\n",
       "2                1           4.0       12353       42     890.000000\n",
       "3                1          58.0       12354       70   10793.999985\n",
       "4                1          13.0       12355       52    4594.000000\n",
       "...            ...           ...         ...      ...            ...\n",
       "2148             6         505.0       15311        1  173184.199470\n",
       "2149             8         392.0       17450        9  339573.900843\n",
       "2150             8          29.0       17949        0  248406.800049\n",
       "2151             7         437.0       14646        2  566736.901106\n",
       "2152             9          91.0       18102       16  611639.300415\n",
       "\n",
       "[2153 rows x 5 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_3m = ed_users.filter(['customer_id', 'OverallScore','recency','count_orders','total_revenue'])\n",
    "es_3m = ed.pandas_to_eland(es_3m,es,\"es-customers-3m-clusters\",es_if_exists=\"replace\",es_refresh=True)\n",
    "es_3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>avg_revenue</th>\n",
       "      <th>count_orders_x</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_invoice</th>\n",
       "      <th>last_invoice</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total_revenue_x</th>\n",
       "      <th>OverallScore</th>\n",
       "      <th>count_orders_y</th>\n",
       "      <th>recency</th>\n",
       "      <th>total_revenue_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.006251</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12347</td>\n",
       "      <td>2019-08-02 08:48:00+00:00</td>\n",
       "      <td>2019-12-07 15:52:00+00:00</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>21040.500061</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10187.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1033.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12348</td>\n",
       "      <td>2019-09-25 13:13:00+00:00</td>\n",
       "      <td>2019-09-25 13:13:00+00:00</td>\n",
       "      <td>217.0</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3670.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240.760274</td>\n",
       "      <td>73.0</td>\n",
       "      <td>12349</td>\n",
       "      <td>2019-11-21 09:51:00+00:00</td>\n",
       "      <td>2019-11-21 09:51:00+00:00</td>\n",
       "      <td>631.0</td>\n",
       "      <td>17575.500008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200.900000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>12352</td>\n",
       "      <td>2019-09-20 14:34:00+00:00</td>\n",
       "      <td>2019-11-03 14:37:00+00:00</td>\n",
       "      <td>282.0</td>\n",
       "      <td>9442.300003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>291.750000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12356</td>\n",
       "      <td>2019-11-17 08:40:00+00:00</td>\n",
       "      <td>2019-11-17 08:40:00+00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>583.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4814.599998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_revenue  count_orders_x customer_id             first_invoice  \\\n",
       "0   263.006251            80.0       12347 2019-08-02 08:48:00+00:00   \n",
       "1  1033.333333             3.0       12348 2019-09-25 13:13:00+00:00   \n",
       "2   240.760274            73.0       12349 2019-11-21 09:51:00+00:00   \n",
       "3   200.900000            47.0       12352 2019-09-20 14:34:00+00:00   \n",
       "4   291.750000             2.0       12356 2019-11-17 08:40:00+00:00   \n",
       "\n",
       "               last_invoice  quantity  total_revenue_x  OverallScore  \\\n",
       "0 2019-12-07 15:52:00+00:00    1145.0     21040.500061           1.0   \n",
       "1 2019-09-25 13:13:00+00:00     217.0      3100.000000           1.0   \n",
       "2 2019-11-21 09:51:00+00:00     631.0     17575.500008           0.0   \n",
       "3 2019-11-03 14:37:00+00:00     282.0      9442.300003           0.0   \n",
       "4 2019-11-17 08:40:00+00:00       5.0       583.500000           1.0   \n",
       "\n",
       "   count_orders_y  recency  total_revenue_y  \n",
       "0            42.0     21.0     10187.700012  \n",
       "1             5.0     86.0      3670.000000  \n",
       "2             0.0      0.0         0.000000  \n",
       "3             0.0      0.0         0.000000  \n",
       "4            21.0     83.0      4814.599998  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_6m = ed.DataFrame(es, 'es-customers-6m')\n",
    "es_6m_pandas = ed.eland_to_pandas(es_6m)\n",
    "es_3m_pandas = ed.eland_to_pandas(es_3m)\n",
    "#merge data\n",
    "d_merge = pd.merge(es_6m_pandas, es_3m_pandas, on='customer_id', how='left')\n",
    "d_merge = d_merge.fillna(0)\n",
    "d_merge.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTVCluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3496.0</td>\n",
       "      <td>3203.261643</td>\n",
       "      <td>5649.860811</td>\n",
       "      <td>-8998.799562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4238.874996</td>\n",
       "      <td>40166.599998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.0</td>\n",
       "      <td>77472.852753</td>\n",
       "      <td>46160.370071</td>\n",
       "      <td>40851.799950</td>\n",
       "      <td>48835.000000</td>\n",
       "      <td>60814.600067</td>\n",
       "      <td>82455.800087</td>\n",
       "      <td>248406.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>469594.950695</td>\n",
       "      <td>139565.613112</td>\n",
       "      <td>339573.900843</td>\n",
       "      <td>355215.750523</td>\n",
       "      <td>463583.300761</td>\n",
       "      <td>577962.500933</td>\n",
       "      <td>611639.300415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count           mean            std            min  \\\n",
       "LTVCluster                                                        \n",
       "0           3496.0    3203.261643    5649.860811   -8998.799562   \n",
       "1             57.0   77472.852753   46160.370071   40851.799950   \n",
       "2              4.0  469594.950695  139565.613112  339573.900843   \n",
       "\n",
       "                      25%            50%            75%            max  \n",
       "LTVCluster                                                              \n",
       "0                0.000000       0.000000    4238.874996   40166.599998  \n",
       "1            48835.000000   60814.600067   82455.800087  248406.800049  \n",
       "2           355215.750523  463583.300761  577962.500933  611639.300415  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove outliers\n",
    "d_merge = d_merge[d_merge['total_revenue_y']<d_merge['total_revenue_y'].quantile(0.99)]\n",
    "\n",
    "\n",
    "#creating 3 clusters\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(tx_merge[['total_revenue_y']])\n",
    "tx_merge['LTVCluster'] = kmeans.predict(tx_merge[['total_revenue_y']])\n",
    "\n",
    "#order cluster number based on LTV\n",
    "tx_merge = order_cluster('LTVCluster', 'total_revenue_y',tx_merge,True)\n",
    "\n",
    "#creatinga new cluster dataframe\n",
    "tx_cluster = tx_merge.copy()\n",
    "\n",
    "#see details of the clusters\n",
    "tx_cluster.groupby('LTVCluster')['total_revenue_y'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>avg_revenue</th>\n",
       "      <th>count_orders_x</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total_revenue_x</th>\n",
       "      <th>OverallScore</th>\n",
       "      <th>recency</th>\n",
       "      <th>count_orders_y</th>\n",
       "      <th>total_revenue_y</th>\n",
       "      <th>LTVCluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.006251</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12347</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>2.104050e+04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10187.700012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1033.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12348</td>\n",
       "      <td>217.0</td>\n",
       "      <td>3.100000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3670.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240.760274</td>\n",
       "      <td>73.0</td>\n",
       "      <td>12349</td>\n",
       "      <td>631.0</td>\n",
       "      <td>1.757550e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200.900000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>12352</td>\n",
       "      <td>282.0</td>\n",
       "      <td>9.442300e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>291.750000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12356</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.835000e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4814.599998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>5396.377283</td>\n",
       "      <td>44.0</td>\n",
       "      <td>17949</td>\n",
       "      <td>16490.0</td>\n",
       "      <td>2.374406e+05</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>248406.800049</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3553</th>\n",
       "      <td>1389.123335</td>\n",
       "      <td>360.0</td>\n",
       "      <td>12415</td>\n",
       "      <td>30132.0</td>\n",
       "      <td>5.000844e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>360429.700417</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>1370.887395</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>14646</td>\n",
       "      <td>99301.0</td>\n",
       "      <td>1.435319e+06</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>566736.901106</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>5954.410708</td>\n",
       "      <td>215.0</td>\n",
       "      <td>17450</td>\n",
       "      <td>46003.0</td>\n",
       "      <td>1.280198e+06</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>339573.900843</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>4792.177311</td>\n",
       "      <td>313.0</td>\n",
       "      <td>18102</td>\n",
       "      <td>40102.0</td>\n",
       "      <td>1.499951e+06</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>611639.300415</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3557 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_revenue  count_orders_x customer_id  quantity  total_revenue_x  \\\n",
       "0      263.006251            80.0       12347    1145.0     2.104050e+04   \n",
       "1     1033.333333             3.0       12348     217.0     3.100000e+03   \n",
       "2      240.760274            73.0       12349     631.0     1.757550e+04   \n",
       "3      200.900000            47.0       12352     282.0     9.442300e+03   \n",
       "4      291.750000             2.0       12356       5.0     5.835000e+02   \n",
       "...           ...             ...         ...       ...              ...   \n",
       "3552  5396.377283            44.0       17949   16490.0     2.374406e+05   \n",
       "3553  1389.123335           360.0       12415   30132.0     5.000844e+05   \n",
       "3554  1370.887395          1047.0       14646   99301.0     1.435319e+06   \n",
       "3555  5954.410708           215.0       17450   46003.0     1.280198e+06   \n",
       "3556  4792.177311           313.0       18102   40102.0     1.499951e+06   \n",
       "\n",
       "      OverallScore  recency  count_orders_y  total_revenue_y  LTVCluster  \n",
       "0              1.0     21.0            42.0     10187.700012           0  \n",
       "1              1.0     86.0             5.0      3670.000000           0  \n",
       "2              0.0      0.0             0.0         0.000000           0  \n",
       "3              0.0      0.0             0.0         0.000000           0  \n",
       "4              1.0     83.0            21.0      4814.599998           0  \n",
       "...            ...      ...             ...              ...         ...  \n",
       "3552           7.0      0.0            29.0    248406.800049           1  \n",
       "3553           3.0     15.0           218.0    360429.700417           2  \n",
       "3554           8.0      2.0           437.0    566736.901106           2  \n",
       "3555           7.0      9.0           392.0    339573.900843           2  \n",
       "3556           8.0     16.0            91.0    611639.300415           2  \n",
       "\n",
       "[3557 rows x 10 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_data = tx_cluster.drop(['first_invoice','last_invoice'],1)\n",
    "dd = ed.pandas_to_eland(ml_data,es,\"ml-customers\")\n",
    "ml_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-44-0478fbb35d26>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-44-0478fbb35d26>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    dd = ed.pandas_to_eland(ml_data,es,\"ml-customers\")\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}