notebook_es_eland.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"cluster_name\": \"churn\",\n",
      "  \"cluster_uuid\": \"K3nB4fp_QcyjpY-e2XVUbA\",\n",
      "  \"name\": \"node-01\",\n",
      "  \"tagline\": \"You Know, for Search\",\n",
      "  \"version\": {\n",
      "    \"build_date\": \"2020-07-22T19:31:37.655268Z\",\n",
      "    \"build_flavor\": \"default\",\n",
      "    \"build_hash\": \"bbbd2282a6668869c41efc5713ad8214d44c0ad1\",\n",
      "    \"build_snapshot\": true,\n",
      "    \"build_type\": \"zip\",\n",
      "    \"lucene_version\": \"8.6.0\",\n",
      "    \"minimum_index_compatibility_version\": \"7.0.0\",\n",
      "    \"minimum_wire_compatibility_version\": \"7.10.0\",\n",
      "    \"number\": \"8.0.0-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=(\"es_kbn\", \"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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 541909 entries, 0 to 541908\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   invoice_id         541909 non-null  object \n",
      " 1   item_id            541909 non-null  int64  \n",
      " 2   item_model         541909 non-null  object \n",
      " 3   item_name          541909 non-null  object \n",
      " 4   item_brand         541909 non-null  object \n",
      " 5   item_vendor        541909 non-null  object \n",
      " 6   order_qty          541909 non-null  int64  \n",
      " 7   invoice_date       541909 non-null  object \n",
      " 8   unit_price         541909 non-null  float64\n",
      " 9   customer_id        541909 non-null  int64  \n",
      " 10  country_name       541909 non-null  object \n",
      " 11  country_latitude   541909 non-null  float64\n",
      " 12  country_longitude  541909 non-null  float64\n",
      "dtypes: float64(3), int64(3), object(7)\n",
      "memory usage: 57.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset from the local csv file of call logs\n",
    "pd_df = pd.read_csv(\"./Desktop/telcos-ecs/eland_es_analytics/invoices.csv\", sep=';', encoding = 'unicode_escape').dropna()\n",
    "pd_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the type of Invoice Date Field from string to datetime.\n",
    "pd_df['invoice_date'] = pd.to_datetime(pd_df['invoice_date'])\n",
    "\n",
    "# Arrange prices for phones\n",
    "pd_df['unit_price'] = pd_df['unit_price'] * 10.00\n",
    "\n",
    "# Rename the columns to be snake_case\n",
    "pd_df.columns = [x.lower().replace(\" \", \"_\") for x in pd_df.columns]\n",
    "\n",
    "# Combine the 'latitude' and 'longitude' columns into one column 'location' for 'geo_point'\n",
    "pd_df[\"country_location\"] = pd_df[[\"country_latitude\", \"country_longitude\"]].apply(lambda x: \",\".join(str(item) for item in x), axis=1)\n",
    "\n",
    "# Drop the old columns in favor of 'location'\n",
    "pd_df.drop([\"country_latitude\", \"country_longitude\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 541909 entries, 0 to 541908\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   invoice_id        541909 non-null  object        \n",
      " 1   item_id           541909 non-null  int64         \n",
      " 2   item_model        541909 non-null  object        \n",
      " 3   item_name         541909 non-null  object        \n",
      " 4   item_brand        541909 non-null  object        \n",
      " 5   item_vendor       541909 non-null  object        \n",
      " 6   order_qty         541909 non-null  int64         \n",
      " 7   invoice_date      541909 non-null  datetime64[ns]\n",
      " 8   unit_price        541909 non-null  float64       \n",
      " 9   customer_id       541909 non-null  int64         \n",
      " 10  country_name      541909 non-null  object        \n",
      " 11  country_location  541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3), object(7)\n",
      "memory usage: 53.7+ MB\n"
     ]
    }
   ],
   "source": [
    "pd_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data into elasticsearch\n",
    "ed_df = ed.pandas_to_eland(\n",
    "    pd_df=pd_df,\n",
    "    es_client=es,\n",
    "\n",
    "    # Where the data will live in Elasticsearch\n",
    "    es_dest_index=\"es-invoices\",\n",
    "\n",
    "    # Type overrides for certain columns, this can be used to customize index mapping before ingest\n",
    "    es_type_overrides={\n",
    "        \"invoice_id\": \"keyword\",\n",
    "        \"item_id\": \"keyword\",\n",
    "        \"item_model\": \"keyword\",\n",
    "        \"item_name\": \"keyword\",     \n",
    "        \"item_brand\": \"keyword\",\n",
    "        \"item_vendor\": \"keyword\",   \n",
    "        \"order_qty\": \"integer\",\n",
    "        \"invoice_date\": \"date\",\n",
    "        \"unit_price\": \"float\",  \n",
    "        \"customer_id\": \"keyword\",\n",
    "        \"country_name\": \"keyword\",\n",
    "        \"country_location\": \"geo_point\"  \n",
    "    },\n",
    "\n",
    "    # If the index already exists what should we do?\n",
    "    es_if_exists=\"replace\",\n",
    "\n",
    "    # Wait for data to be indexed before returning\n",
    "    es_refresh=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'eland.dataframe.DataFrame'>\n",
      "Index: 28 entries, 0 to 27\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   country_location  28 non-null     object        \n",
      " 1   country_name      28 non-null     object        \n",
      " 2   customer_id       28 non-null     object        \n",
      " 3   invoice_date      28 non-null     datetime64[ns]\n",
      " 4   invoice_id        28 non-null     object        \n",
      " 5   item_brand        28 non-null     object        \n",
      " 6   item_id           28 non-null     object        \n",
      " 7   item_model        28 non-null     object        \n",
      " 8   item_name         28 non-null     object        \n",
      " 9   item_vendor       28 non-null     object        \n",
      " 10  order_qty         28 non-null     int64         \n",
      " 11  unit_price        28 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(9)\n",
      "memory usage: 64.0 bytes\n"
     ]
    }
   ],
   "source": [
    "ed_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"es-invoices\": {\n",
      "    \"mappings\": {\n",
      "      \"properties\": {\n",
      "        \"country_location\": {\n",
      "          \"type\": \"geo_point\"\n",
      "        },\n",
      "        \"country_name\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"customer_id\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"invoice_date\": {\n",
      "          \"type\": \"date\"\n",
      "        },\n",
      "        \"invoice_id\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"item_brand\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"item_id\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"item_model\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"item_name\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"item_vendor\": {\n",
      "          \"type\": \"keyword\"\n",
      "        },\n",
      "        \"order_qty\": {\n",
      "          \"type\": \"integer\"\n",
      "        },\n",
      "        \"unit_price\": {\n",
      "          \"type\": \"float\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "json(es.indices.get_mapping(index=\"es-invoices\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'eland.dataframe.DataFrame'>\n",
      "Index: 28 entries, 0 to 27\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   country_latitude   28 non-null     float64\n",
      " 1   country_longitude  28 non-null     float64\n",
      " 2   country_name       28 non-null     object \n",
      " 3   customer_id        28 non-null     object \n",
      " 4   invoice_date       28 non-null     object \n",
      " 5   invoice_id         28 non-null     object \n",
      " 6   item_brand         28 non-null     object \n",
      " 7   item_id            28 non-null     object \n",
      " 8   item_model         28 non-null     object \n",
      " 9   item_name          28 non-null     object \n",
      " 10  item_vendor        28 non-null     object \n",
      " 11  order_qty          28 non-null     int64  \n",
      " 12  unit_price         28 non-null     float64\n",
      "dtypes: float64(3), int64(1), object(9)\n",
      "memory usage: 64.0 bytes\n"
     ]
    }
   ],
   "source": [
    "ed_df = ed.read_csv(\"./Desktop/telcos-ecs/eland_es_analytics/invoices_dataset.csv\",\n",
    "                es_client=es,\n",
    "\n",
    "    # Where the data will live in Elasticsearch\n",
    "    es_dest_index=\"es-invoices\",\n",
    "\n",
    "    # Type overrides for certain columns, this can be used to customize index mapping before ingest\n",
    "    es_type_overrides={\n",
    "        \"invoice_id\": \"keyword\",\n",
    "        \"item_id\": \"keyword\",\n",
    "        \"item_model\": \"keyword\",\n",
    "        \"item_name\": \"keyword\",     \n",
    "        \"item_brand\": \"keyword\",\n",
    "        \"item_vendor\": \"keyword\",   \n",
    "        \"order_qty\": \"integer\",\n",
    "        \"invoice_date\": \"keyword\",\n",
    "        \"unit_price\": \"float\",  \n",
    "        \"customer_id\": \"keyword\",\n",
    "        \"country_name\": \"keyword\",\n",
    "        \"country_location\": \"geo_point\"  \n",
    "    },\n",
    "\n",
    "    # If the index already exists what should we do?\n",
    "    es_if_exists=\"replace\",\n",
    "\n",
    "    # Wait for data to be indexed before returning\n",
    "    es_refresh=True,\n",
    ")\n",
    "ed_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'_id'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_df.index.es_index_field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_location', 'country_name', 'customer_id', 'invoice_date',\n",
       "       'invoice_id', 'item_brand', 'item_id', 'item_model', 'item_name',\n",
       "       'item_vendor', 'order_qty', 'unit_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>46.111193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.080957</td>\n",
       "      <td>967.597634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-110620.601562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>41.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>389700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_qty     unit_price\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250      46.111193\n",
       "std       218.080957     967.597634\n",
       "min    -80995.000000 -110620.601562\n",
       "25%         1.000000      12.500000\n",
       "50%         3.000000      20.799999\n",
       "75%        10.000000      41.299999\n",
       "max     80995.000000  389700.000000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregating values\n",
    "ed_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 12)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape is determined by using count API\n",
    "ed_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_invoices = ed.read_es(es, 'es-invoices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_vendor</th>\n",
       "      <th>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138000</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35310308</td>\n",
       "      <td>SM-J110H/DS</td>\n",
       "      <td>Samsung SM-J110H/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138001</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35271109</td>\n",
       "      <td>SM-G960F/DS</td>\n",
       "      <td>GALAXY S9 SM-G960F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138002</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35283806</td>\n",
       "      <td>GT-I8262</td>\n",
       "      <td>GALAXY CORE GT-I8262</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138003</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86964104</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>12</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138004</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86094104</td>\n",
       "      <td>M1901F7G</td>\n",
       "      <td>Redmi Note7</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>48</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "138000  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138001  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138002  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138003  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138004  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "\n",
       "       invoice_id item_brand   item_id   item_model              item_name  \\\n",
       "138000     553464    Samsung  35310308  SM-J110H/DS    Samsung SM-J110H/DS   \n",
       "138001     553464    Samsung  35271109  SM-G960F/DS  GALAXY S9 SM-G960F/DS   \n",
       "138002     553464    Samsung  35283806     GT-I8262   GALAXY CORE GT-I8262   \n",
       "138003     553464      Redmi  86964104    M1908C3JG           Redmi Note 8   \n",
       "138004     553464      Redmi  86094104     M1901F7G            Redmi Note7   \n",
       "\n",
       "                         item_vendor  order_qty  unit_price  \n",
       "138000                 Samsung Korea         12        16.5  \n",
       "138001                 Samsung Korea         12        14.5  \n",
       "138002                 Samsung Korea          4        32.9  \n",
       "138003  Xiaomi Communications Co Ltd         12        81.5  \n",
       "138004  Xiaomi Communications Co Ltd         48         8.5  \n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_invoices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'eland.dataframe.DataFrame'>\n",
      "Index: 541909 entries, 1500 to 541908\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   country_location  541909 non-null  object        \n",
      " 1   country_name      541909 non-null  object        \n",
      " 2   customer_id       541909 non-null  object        \n",
      " 3   invoice_date      541909 non-null  datetime64[ns]\n",
      " 4   invoice_id        541909 non-null  object        \n",
      " 5   item_brand        541909 non-null  object        \n",
      " 6   item_id           541909 non-null  object        \n",
      " 7   item_model        541909 non-null  object        \n",
      " 8   item_name         541909 non-null  object        \n",
      " 9   item_vendor       541909 non-null  object        \n",
      " 10  order_qty         541909 non-null  int64         \n",
      " 11  unit_price        541909 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(9)\n",
      "memory usage: 64.0 bytes\n"
     ]
    }
   ],
   "source": [
    "ed_invoices.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_vendor</th>\n",
       "      <th>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138000</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35310308</td>\n",
       "      <td>SM-J110H/DS</td>\n",
       "      <td>Samsung SM-J110H/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138001</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35271109</td>\n",
       "      <td>SM-G960F/DS</td>\n",
       "      <td>GALAXY S9 SM-G960F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138002</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35283806</td>\n",
       "      <td>GT-I8262</td>\n",
       "      <td>GALAXY CORE GT-I8262</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138003</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86964104</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>12</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138004</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86094104</td>\n",
       "      <td>M1901F7G</td>\n",
       "      <td>Redmi Note7</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>48</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138005</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35463609</td>\n",
       "      <td>SM-J701F/DS</td>\n",
       "      <td>Galaxy J7</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>36</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138006</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35264811</td>\n",
       "      <td>SM-T515</td>\n",
       "      <td>Galaxy Tab A 2019</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138007</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:09:00</td>\n",
       "      <td>553465</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86169704</td>\n",
       "      <td>M1906G7G</td>\n",
       "      <td>Redmi Note 8 Pro</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>80</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138008</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-06-07 15:28:00</td>\n",
       "      <td>C555877</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86964104</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>-1</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138009</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-06-07 15:28:00</td>\n",
       "      <td>C555877</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35271109</td>\n",
       "      <td>SM-G960F/DS</td>\n",
       "      <td>GALAXY S9 SM-G960F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>-2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>10 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "138000  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138001  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138002  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138003  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138004  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138005  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138006  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138007  31.791702,-7.09262      Morocco        16218 2019-05-17 11:09:00   \n",
       "138008  31.791702,-7.09262      Morocco        16218 2019-06-07 15:28:00   \n",
       "138009  31.791702,-7.09262      Morocco        16218 2019-06-07 15:28:00   \n",
       "\n",
       "       invoice_id item_brand   item_id   item_model              item_name  \\\n",
       "138000     553464    Samsung  35310308  SM-J110H/DS    Samsung SM-J110H/DS   \n",
       "138001     553464    Samsung  35271109  SM-G960F/DS  GALAXY S9 SM-G960F/DS   \n",
       "138002     553464    Samsung  35283806     GT-I8262   GALAXY CORE GT-I8262   \n",
       "138003     553464      Redmi  86964104    M1908C3JG           Redmi Note 8   \n",
       "138004     553464      Redmi  86094104     M1901F7G            Redmi Note7   \n",
       "138005     553464    Samsung  35463609  SM-J701F/DS              Galaxy J7   \n",
       "138006     553464    Samsung  35264811      SM-T515      Galaxy Tab A 2019   \n",
       "138007     553465      Redmi  86169704     M1906G7G       Redmi Note 8 Pro   \n",
       "138008    C555877      Redmi  86964104    M1908C3JG           Redmi Note 8   \n",
       "138009    C555877    Samsung  35271109  SM-G960F/DS  GALAXY S9 SM-G960F/DS   \n",
       "\n",
       "                         item_vendor  order_qty  unit_price  \n",
       "138000                 Samsung Korea         12        16.5  \n",
       "138001                 Samsung Korea         12        14.5  \n",
       "138002                 Samsung Korea          4        32.9  \n",
       "138003  Xiaomi Communications Co Ltd         12        81.5  \n",
       "138004  Xiaomi Communications Co Ltd         48         8.5  \n",
       "138005                 Samsung Korea         36        12.5  \n",
       "138006                 Samsung Korea         12        14.5  \n",
       "138007  Xiaomi Communications Co Ltd         80        14.5  \n",
       "138008  Xiaomi Communications Co Ltd         -1        81.5  \n",
       "138009                 Samsung Korea         -2        14.5  \n",
       "\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "ed_invoices.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_location', 'country_name', 'customer_id', 'invoice_date',\n",
       "       'invoice_id', 'item_brand', 'item_id', 'item_model', 'item_name',\n",
       "       'item_vendor', 'order_qty', 'unit_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "ed_invoices.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138000    16218\n",
       "138001    16218\n",
       "138002    16218\n",
       "138003    16218\n",
       "138004    16218\n",
       "          ...  \n",
       "541904    16265\n",
       "541905    16262\n",
       "541906    16262\n",
       "541907    16262\n",
       "541908    16261\n",
       "Name: customer_id, Length: 541909, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "ed_invoices.customer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138000    553464\n",
       "138001    553464\n",
       "138002    553464\n",
       "138003    553464\n",
       "138004    553464\n",
       "           ...  \n",
       "541904    581498\n",
       "541905    581498\n",
       "541906    581498\n",
       "541907    581498\n",
       "541908    581498\n",
       "Name: invoice_id, Length: 541909, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "ed_invoices[\"invoice_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
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       "      <th>unit_price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138000</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35310308</td>\n",
       "      <td>SM-J110H/DS</td>\n",
       "      <td>Samsung SM-J110H/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138001</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35271109</td>\n",
       "      <td>SM-G960F/DS</td>\n",
       "      <td>GALAXY S9 SM-G960F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138002</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35283806</td>\n",
       "      <td>GT-I8262</td>\n",
       "      <td>GALAXY CORE GT-I8262</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138003</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86964104</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>12</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138004</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86094104</td>\n",
       "      <td>M1901F7G</td>\n",
       "      <td>Redmi Note7</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>48</td>\n",
       "      <td>8.5</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16265</td>\n",
       "      <td>2019-12-09 10:26:00</td>\n",
       "      <td>581498</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86041704</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>5</td>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16262</td>\n",
       "      <td>2019-12-09 10:26:00</td>\n",
       "      <td>581498</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86403204</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>4</td>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16262</td>\n",
       "      <td>2019-12-09 10:26:00</td>\n",
       "      <td>581498</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35159910</td>\n",
       "      <td>SM-J410F/DS</td>\n",
       "      <td>GALAXY J4 CORE SM-J410F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>1</td>\n",
       "      <td>49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16262</td>\n",
       "      <td>2019-12-09 10:26:00</td>\n",
       "      <td>581498</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35438811</td>\n",
       "      <td>SM-A315F/DS</td>\n",
       "      <td>Galaxy A31</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>1</td>\n",
       "      <td>107.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16261</td>\n",
       "      <td>2019-12-09 10:26:00</td>\n",
       "      <td>581498</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86723304</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>17141.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>509604 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "138000  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138001  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138002  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138003  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138004  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "...                    ...          ...          ...                 ...   \n",
       "541904  31.791702,-7.09262      Morocco        16265 2019-12-09 10:26:00   \n",
       "541905  31.791702,-7.09262      Morocco        16262 2019-12-09 10:26:00   \n",
       "541906  31.791702,-7.09262      Morocco        16262 2019-12-09 10:26:00   \n",
       "541907  31.791702,-7.09262      Morocco        16262 2019-12-09 10:26:00   \n",
       "541908  31.791702,-7.09262      Morocco        16261 2019-12-09 10:26:00   \n",
       "\n",
       "       invoice_id item_brand   item_id   item_model  \\\n",
       "138000     553464    Samsung  35310308  SM-J110H/DS   \n",
       "138001     553464    Samsung  35271109  SM-G960F/DS   \n",
       "138002     553464    Samsung  35283806     GT-I8262   \n",
       "138003     553464      Redmi  86964104    M1908C3JG   \n",
       "138004     553464      Redmi  86094104     M1901F7G   \n",
       "...           ...        ...       ...          ...   \n",
       "541904     581498      Redmi  86041704    M1908C3IG   \n",
       "541905     581498      Redmi  86403204    M1908C3JG   \n",
       "541906     581498    Samsung  35159910  SM-J410F/DS   \n",
       "541907     581498    Samsung  35438811  SM-A315F/DS   \n",
       "541908     581498      Redmi  86723304    M1908C3IG   \n",
       "\n",
       "                         item_name                   item_vendor  order_qty  \\\n",
       "138000         Samsung SM-J110H/DS                 Samsung Korea         12   \n",
       "138001       GALAXY S9 SM-G960F/DS                 Samsung Korea         12   \n",
       "138002        GALAXY CORE GT-I8262                 Samsung Korea          4   \n",
       "138003                Redmi Note 8  Xiaomi Communications Co Ltd         12   \n",
       "138004                 Redmi Note7  Xiaomi Communications Co Ltd         48   \n",
       "...                            ...                           ...        ...   \n",
       "541904                     Redmi 8  Xiaomi Communications Co Ltd          5   \n",
       "541905                Redmi Note 8  Xiaomi Communications Co Ltd          4   \n",
       "541906  GALAXY J4 CORE SM-J410F/DS                 Samsung Korea          1   \n",
       "541907                  Galaxy A31                 Samsung Korea          1   \n",
       "541908                     Redmi 8  Xiaomi Communications Co Ltd          1   \n",
       "\n",
       "        unit_price  \n",
       "138000        16.5  \n",
       "138001        14.5  \n",
       "138002        32.9  \n",
       "138003        81.5  \n",
       "138004         8.5  \n",
       "...            ...  \n",
       "541904        41.3  \n",
       "541905        41.3  \n",
       "541906        49.6  \n",
       "541907       107.9  \n",
       "541908     17141.7  \n",
       "\n",
       "[509604 rows x 12 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "ed_invoices[ed_invoices.item_brand.isin([\"Redmi\", \"Samsung\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "es_index_pattern: es-invoices\n",
      "Index:\n",
      " es_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "                     es_field_name  is_source   es_dtype es_date_format        pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_es_field_name\n",
      "country_location  country_location       True  geo_point           None          object           True             True        False           country_location\n",
      "country_name          country_name       True    keyword           None          object           True             True        False               country_name\n",
      "customer_id            customer_id       True    keyword           None          object           True             True        False                customer_id\n",
      "invoice_date          invoice_date       True       date           None  datetime64[ns]           True             True        False               invoice_date\n",
      "invoice_id              invoice_id       True    keyword           None          object           True             True        False                 invoice_id\n",
      "item_brand              item_brand       True    keyword           None          object           True             True        False                 item_brand\n",
      "item_id                    item_id       True    keyword           None          object           True             True        False                    item_id\n",
      "item_model              item_model       True    keyword           None          object           True             True        False                 item_model\n",
      "item_name                item_name       True    keyword           None          object           True             True        False                  item_name\n",
      "item_vendor            item_vendor       True    keyword           None          object           True             True        False                item_vendor\n",
      "order_qty                order_qty       True    integer           None           int64           True             True        False                  order_qty\n",
      "unit_price              unit_price       True      float           None         float64           True             True        False                 unit_price\n",
      "Operations:\n",
      " tasks: [('boolean_filter': ('boolean_filter': {'terms': {'item_brand': ['Redmi', 'Samsung']}}))]\n",
      " size: None\n",
      " sort_params: None\n",
      " _source: ['country_location', 'country_name', 'customer_id', 'invoice_date', 'invoice_id', 'item_brand', 'item_id', 'item_model', 'item_name', 'item_vendor', 'order_qty', 'unit_price']\n",
      " body: {'query': {'terms': {'item_brand': ['Redmi', 'Samsung']}}}\n",
      " post_processing: []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "print(ed_invoices[ed_invoices.item_brand.isin([\"Redmi\", \"Samsung\"])].es_info())\n",
    "#print(df.tail(10).es_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "es_index_pattern: es-invoices\n",
      "Index:\n",
      " es_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "                     es_field_name  is_source   es_dtype es_date_format        pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_es_field_name\n",
      "country_location  country_location       True  geo_point           None          object           True             True        False           country_location\n",
      "country_name          country_name       True    keyword           None          object           True             True        False               country_name\n",
      "customer_id            customer_id       True    keyword           None          object           True             True        False                customer_id\n",
      "invoice_date          invoice_date       True       date           None  datetime64[ns]           True             True        False               invoice_date\n",
      "invoice_id              invoice_id       True    keyword           None          object           True             True        False                 invoice_id\n",
      "item_brand              item_brand       True    keyword           None          object           True             True        False                 item_brand\n",
      "item_id                    item_id       True    keyword           None          object           True             True        False                    item_id\n",
      "item_model              item_model       True    keyword           None          object           True             True        False                 item_model\n",
      "item_name                item_name       True    keyword           None          object           True             True        False                  item_name\n",
      "item_vendor            item_vendor       True    keyword           None          object           True             True        False                item_vendor\n",
      "order_qty                order_qty       True    integer           None           int64           True             True        False                  order_qty\n",
      "unit_price              unit_price       True      float           None         float64           True             True        False                 unit_price\n",
      "Operations:\n",
      " tasks: [('tail': ('sort_field': '_doc', 'count': 10))]\n",
      " size: 10\n",
      " sort_params: _doc:desc\n",
      " _source: ['country_location', 'country_name', 'customer_id', 'invoice_date', 'invoice_id', 'item_brand', 'item_id', 'item_model', 'item_name', 'item_vendor', 'order_qty', 'unit_price']\n",
      " body: {}\n",
      " post_processing: [('sort_index')]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DataFrame has many APIs compatible with Pandas\n",
    "print(ed_invoices.tail(10).es_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>46.111193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.080957</td>\n",
       "      <td>967.597634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-110620.601562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>41.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>389700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_qty     unit_price\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250      46.111193\n",
       "std       218.080957     967.597634\n",
       "min    -80995.000000 -110620.601562\n",
       "25%         1.000000      12.500000\n",
       "50%         3.000000      20.799999\n",
       "75%        10.000000      41.299999\n",
       "max     80995.000000  389700.000000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregating values\n",
    "ed_invoices.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting with matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "ed_invoices[[\"order_qty\"]].hist(figsize=[10,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>country_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_vendor</th>\n",
       "      <th>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138080</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16216</td>\n",
       "      <td>2019-03-17 15:34:00</td>\n",
       "      <td>546862</td>\n",
       "      <td>TP-LINK</td>\n",
       "      <td>86872502</td>\n",
       "      <td>M7300</td>\n",
       "      <td>TP-LINK M7300</td>\n",
       "      <td>TP-Link Technologies Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>1650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137099</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16240</td>\n",
       "      <td>2019-09-23 13:44:00</td>\n",
       "      <td>568065</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35862706</td>\n",
       "      <td>SM-G355H/DS</td>\n",
       "      <td>Samsung SM-G355H/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142048</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16143</td>\n",
       "      <td>2018-12-20 12:51:00</td>\n",
       "      <td>539581</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35898907</td>\n",
       "      <td>SM-A310F/DS</td>\n",
       "      <td>Samsung SM-A310F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142456</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16133</td>\n",
       "      <td>2019-05-03 10:42:00</td>\n",
       "      <td>551559</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237711</td>\n",
       "      <td>X10</td>\n",
       "      <td>SEASHARK, X10</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148580</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16029</td>\n",
       "      <td>2019-09-13 17:28:00</td>\n",
       "      <td>C566614</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35631908</td>\n",
       "      <td>SM-A320F/DS</td>\n",
       "      <td>GALAXY A3 SM-A320F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>-1</td>\n",
       "      <td>1022.4</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507511</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16806</td>\n",
       "      <td>2019-10-10 16:02:00</td>\n",
       "      <td>570465</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86723304</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>1884.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507920</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16800</td>\n",
       "      <td>2019-10-11 16:29:00</td>\n",
       "      <td>570688</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86723304</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>1990.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508169</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16794</td>\n",
       "      <td>2019-10-11 16:37:00</td>\n",
       "      <td>570690</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86723304</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509637</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16777</td>\n",
       "      <td>2019-10-17 15:14:00</td>\n",
       "      <td>C571501</td>\n",
       "      <td>RIVO</td>\n",
       "      <td>35272707</td>\n",
       "      <td>RHYTHM RX150</td>\n",
       "      <td>RIVO RHYTHM RX150</td>\n",
       "      <td>Rivo Mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>1531.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509960</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16773</td>\n",
       "      <td>2019-10-17 15:27:00</td>\n",
       "      <td>571508</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86723304</td>\n",
       "      <td>M1908C3IG</td>\n",
       "      <td>Redmi 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>1950.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>476 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "138080  31.791702,-7.09262      Morocco        16216 2019-03-17 15:34:00   \n",
       "137099  31.791702,-7.09262      Morocco        16240 2019-09-23 13:44:00   \n",
       "142048  31.791702,-7.09262      Morocco        16143 2018-12-20 12:51:00   \n",
       "142456  31.791702,-7.09262      Morocco        16133 2019-05-03 10:42:00   \n",
       "148580  31.791702,-7.09262      Morocco        16029 2019-09-13 17:28:00   \n",
       "...                    ...          ...          ...                 ...   \n",
       "507511  31.791702,-7.09262      Morocco        16806 2019-10-10 16:02:00   \n",
       "507920  31.791702,-7.09262      Morocco        16800 2019-10-11 16:29:00   \n",
       "508169  31.791702,-7.09262      Morocco        16794 2019-10-11 16:37:00   \n",
       "509637  31.791702,-7.09262      Morocco        16777 2019-10-17 15:14:00   \n",
       "509960  31.791702,-7.09262      Morocco        16773 2019-10-17 15:27:00   \n",
       "\n",
       "       invoice_id item_brand   item_id    item_model              item_name  \\\n",
       "138080     546862    TP-LINK  86872502         M7300          TP-LINK M7300   \n",
       "137099     568065    Samsung  35862706   SM-G355H/DS    Samsung SM-G355H/DS   \n",
       "142048     539581    Samsung  35898907   SM-A310F/DS    Samsung SM-A310F/DS   \n",
       "142456     551559   SEASHARK  35237711           X10          SEASHARK, X10   \n",
       "148580    C566614    Samsung  35631908   SM-A320F/DS  GALAXY A3 SM-A320F/DS   \n",
       "...           ...        ...       ...           ...                    ...   \n",
       "507511     570465      Redmi  86723304     M1908C3IG                Redmi 8   \n",
       "507920     570688      Redmi  86723304     M1908C3IG                Redmi 8   \n",
       "508169     570690      Redmi  86723304     M1908C3IG                Redmi 8   \n",
       "509637    C571501       RIVO  35272707  RHYTHM RX150      RIVO RHYTHM RX150   \n",
       "509960     571508      Redmi  86723304     M1908C3IG                Redmi 8   \n",
       "\n",
       "                         item_vendor  order_qty  unit_price  \n",
       "138080   TP-Link Technologies Co Ltd          1      1650.0  \n",
       "137099                 Samsung Korea          2      1450.0  \n",
       "142048                 Samsung Korea          2      1250.0  \n",
       "142456             Kamil Trade Links          2      1100.0  \n",
       "148580                 Samsung Korea         -1      1022.4  \n",
       "...                              ...        ...         ...  \n",
       "507511  Xiaomi Communications Co Ltd          1      1884.6  \n",
       "507920  Xiaomi Communications Co Ltd          1      1990.4  \n",
       "508169  Xiaomi Communications Co Ltd          1      1998.1  \n",
       "509637                   Rivo Mobile         -1      1531.3  \n",
       "509960  Xiaomi Communications Co Ltd          1      1950.6  \n",
       "\n",
       "[476 rows x 12 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# es_query() allows for the full Elasticsearch querying capabilities\n",
    "ed_invoices.es_query({\n",
    "  \"range\": {\n",
    "    \"unit_price\": {\n",
    "      \"gte\": 1000,\n",
    "      \"lte\": 2000\n",
    "    }\n",
    "  }\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_vendor</th>\n",
       "      <th>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>142456</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16133</td>\n",
       "      <td>2019-05-03 10:42:00</td>\n",
       "      <td>551559</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237711</td>\n",
       "      <td>X10</td>\n",
       "      <td>SEASHARK, X10</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>2</td>\n",
       "      <td>1100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144630</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16096</td>\n",
       "      <td>2019-03-20 13:38:00</td>\n",
       "      <td>547064</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237711</td>\n",
       "      <td>X10</td>\n",
       "      <td>SEASHARK, X10</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>1</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153363</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>15976</td>\n",
       "      <td>2019-05-03 17:12:00</td>\n",
       "      <td>551724</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35238011</td>\n",
       "      <td>P30</td>\n",
       "      <td>SEASHARK, P30</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>2</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157513</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>15867</td>\n",
       "      <td>2018-12-05 14:17:00</td>\n",
       "      <td>537200</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35238011</td>\n",
       "      <td>P30</td>\n",
       "      <td>SEASHARK, P30</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>3</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181136</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>15524</td>\n",
       "      <td>2019-11-15 12:09:00</td>\n",
       "      <td>576512</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237711</td>\n",
       "      <td>X10</td>\n",
       "      <td>SEASHARK, X10</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>4</td>\n",
       "      <td>1100.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446665</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>17730</td>\n",
       "      <td>2019-03-17 18:15:00</td>\n",
       "      <td>546888</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237911</td>\n",
       "      <td>S10+</td>\n",
       "      <td>SEASHARK, S10+</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446827</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>17730</td>\n",
       "      <td>2019-03-17 18:18:00</td>\n",
       "      <td>546891</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237911</td>\n",
       "      <td>S10+</td>\n",
       "      <td>SEASHARK, S10+</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447880</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>17716</td>\n",
       "      <td>2019-03-20 11:00:00</td>\n",
       "      <td>547042</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237911</td>\n",
       "      <td>S10+</td>\n",
       "      <td>SEASHARK, S10+</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>2</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448089</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>17711</td>\n",
       "      <td>2019-03-22 09:23:00</td>\n",
       "      <td>547248</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237911</td>\n",
       "      <td>S10+</td>\n",
       "      <td>SEASHARK, S10+</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>2</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448288</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>17706</td>\n",
       "      <td>2019-03-22 09:27:00</td>\n",
       "      <td>547249</td>\n",
       "      <td>SEASHARK</td>\n",
       "      <td>35237911</td>\n",
       "      <td>S10+</td>\n",
       "      <td>SEASHARK, S10+</td>\n",
       "      <td>Kamil Trade Links</td>\n",
       "      <td>49</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>75 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "142456  31.791702,-7.09262      Morocco        16133 2019-05-03 10:42:00   \n",
       "144630  31.791702,-7.09262      Morocco        16096 2019-03-20 13:38:00   \n",
       "153363  31.791702,-7.09262      Morocco        15976 2019-05-03 17:12:00   \n",
       "157513  31.791702,-7.09262      Morocco        15867 2018-12-05 14:17:00   \n",
       "181136  31.791702,-7.09262      Morocco        15524 2019-11-15 12:09:00   \n",
       "...                    ...          ...          ...                 ...   \n",
       "446665  31.791702,-7.09262      Morocco        17730 2019-03-17 18:15:00   \n",
       "446827  31.791702,-7.09262      Morocco        17730 2019-03-17 18:18:00   \n",
       "447880  31.791702,-7.09262      Morocco        17716 2019-03-20 11:00:00   \n",
       "448089  31.791702,-7.09262      Morocco        17711 2019-03-22 09:23:00   \n",
       "448288  31.791702,-7.09262      Morocco        17706 2019-03-22 09:27:00   \n",
       "\n",
       "       invoice_id item_brand   item_id item_model       item_name  \\\n",
       "142456     551559   SEASHARK  35237711        X10   SEASHARK, X10   \n",
       "144630     547064   SEASHARK  35237711        X10   SEASHARK, X10   \n",
       "153363     551724   SEASHARK  35238011        P30   SEASHARK, P30   \n",
       "157513     537200   SEASHARK  35238011        P30   SEASHARK, P30   \n",
       "181136     576512   SEASHARK  35237711        X10   SEASHARK, X10   \n",
       "...           ...        ...       ...        ...             ...   \n",
       "446665     546888   SEASHARK  35237911       S10+  SEASHARK, S10+   \n",
       "446827     546891   SEASHARK  35237911       S10+  SEASHARK, S10+   \n",
       "447880     547042   SEASHARK  35237911       S10+  SEASHARK, S10+   \n",
       "448089     547248   SEASHARK  35237911       S10+  SEASHARK, S10+   \n",
       "448288     547249   SEASHARK  35237911       S10+  SEASHARK, S10+   \n",
       "\n",
       "              item_vendor  order_qty  unit_price  \n",
       "142456  Kamil Trade Links          2      1100.0  \n",
       "144630  Kamil Trade Links          1      1250.0  \n",
       "153363  Kamil Trade Links          2         8.3  \n",
       "157513  Kamil Trade Links          3        14.9  \n",
       "181136  Kamil Trade Links          4      1100.0  \n",
       "...                   ...        ...         ...  \n",
       "446665  Kamil Trade Links          6         8.3  \n",
       "446827  Kamil Trade Links          6         8.3  \n",
       "447880  Kamil Trade Links          2         8.3  \n",
       "448089  Kamil Trade Links          2         8.3  \n",
       "448288  Kamil Trade Links         49         4.3  \n",
       "\n",
       "[75 rows x 12 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match query example\n",
    "ed_invoices.es_query({\"match\": {\"item_brand\": \"SEASHARK\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 50 entries, 106944 to 95914\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   country_location  50 non-null     object        \n",
      " 1   country_name      50 non-null     object        \n",
      " 2   customer_id       50 non-null     int64         \n",
      " 3   invoice_date      50 non-null     datetime64[ns]\n",
      " 4   invoice_id        50 non-null     object        \n",
      " 5   item_brand        50 non-null     object        \n",
      " 6   item_id           50 non-null     int64         \n",
      " 7   item_model        50 non-null     object        \n",
      " 8   item_name         50 non-null     object        \n",
      " 9   item_vendor       50 non-null     object        \n",
      " 10  order_qty         50 non-null     int64         \n",
      " 11  unit_price        50 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3), object(7)\n",
      "memory usage: 5.1+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Pull a subset of your data for building graphs / operations locally.\n",
    "sample_df = ed_invoices[ed_invoices.item_brand == \"SEASHARK\"].sample(50).to_pandas()\n",
    "sample_df.info()\n",
    "print(type(sample_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_location            object\n",
       "country_name                object\n",
       "customer_id                 object\n",
       "invoice_date        datetime64[ns]\n",
       "invoice_id                  object\n",
       "item_brand                  object\n",
       "item_id                     object\n",
       "item_model                  object\n",
       "item_name                   object\n",
       "item_vendor                 object\n",
       "order_qty                    int64\n",
       "unit_price                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let’s see the data types of our fields. Running df.dtypes,\n",
    "# we can see that elasticsearch field types are mapped to pandas field types.\n",
    "ed_invoices.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "es_index_pattern: es-invoices\n",
      "Index:\n",
      " es_index_field: _id\n",
      " is_source_field: False\n",
      "Mappings:\n",
      " capabilities:\n",
      "                     es_field_name  is_source   es_dtype es_date_format        pd_dtype  is_searchable  is_aggregatable  is_scripted aggregatable_es_field_name\n",
      "country_location  country_location       True  geo_point           None          object           True             True        False           country_location\n",
      "country_name          country_name       True    keyword           None          object           True             True        False               country_name\n",
      "customer_id            customer_id       True    keyword           None          object           True             True        False                customer_id\n",
      "invoice_date          invoice_date       True       date           None  datetime64[ns]           True             True        False               invoice_date\n",
      "invoice_id              invoice_id       True    keyword           None          object           True             True        False                 invoice_id\n",
      "item_brand              item_brand       True    keyword           None          object           True             True        False                 item_brand\n",
      "item_id                    item_id       True    keyword           None          object           True             True        False                    item_id\n",
      "item_model              item_model       True    keyword           None          object           True             True        False                 item_model\n",
      "item_name                item_name       True    keyword           None          object           True             True        False                  item_name\n",
      "item_vendor            item_vendor       True    keyword           None          object           True             True        False                item_vendor\n",
      "order_qty                order_qty       True    integer           None           int64           True             True        False                  order_qty\n",
      "unit_price              unit_price       True      float           None         float64           True             True        False                 unit_price\n",
      "Operations:\n",
      " tasks: []\n",
      " size: None\n",
      " sort_params: None\n",
      " _source: ['country_location', 'country_name', 'customer_id', 'invoice_date', 'invoice_id', 'item_brand', 'item_id', 'item_model', 'item_name', 'item_vendor', 'order_qty', 'unit_price']\n",
      " body: {}\n",
      " post_processing: []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We also offer a .es_info() data frame method that shows all info about the underlying index.\n",
    "# It also contains information about operations being passed from data frame methods to elasticsearch.\n",
    "# More on this later.\n",
    "print(ed_invoices.es_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'term': {'country_name': 'Morocco'}}\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_location</th>\n",
       "      <th>country_name</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_brand</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_model</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_vendor</th>\n",
       "      <th>order_qty</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138000</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35310308</td>\n",
       "      <td>SM-J110H/DS</td>\n",
       "      <td>Samsung SM-J110H/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138001</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35271109</td>\n",
       "      <td>SM-G960F/DS</td>\n",
       "      <td>GALAXY S9 SM-G960F/DS</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>12</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138002</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>35283806</td>\n",
       "      <td>GT-I8262</td>\n",
       "      <td>GALAXY CORE GT-I8262</td>\n",
       "      <td>Samsung Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138003</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86964104</td>\n",
       "      <td>M1908C3JG</td>\n",
       "      <td>Redmi Note 8</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>12</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138004</th>\n",
       "      <td>31.791702,-7.09262</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>16218</td>\n",
       "      <td>2019-05-17 11:07:00</td>\n",
       "      <td>553464</td>\n",
       "      <td>Redmi</td>\n",
       "      <td>86094104</td>\n",
       "      <td>M1901F7G</td>\n",
       "      <td>Redmi Note7</td>\n",
       "      <td>Xiaomi Communications Co Ltd</td>\n",
       "      <td>48</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<p>5 rows × 12 columns</p>"
      ],
      "text/plain": [
       "          country_location country_name  customer_id        invoice_date  \\\n",
       "138000  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138001  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138002  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138003  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "138004  31.791702,-7.09262      Morocco        16218 2019-05-17 11:07:00   \n",
       "\n",
       "       invoice_id item_brand   item_id   item_model              item_name  \\\n",
       "138000     553464    Samsung  35310308  SM-J110H/DS    Samsung SM-J110H/DS   \n",
       "138001     553464    Samsung  35271109  SM-G960F/DS  GALAXY S9 SM-G960F/DS   \n",
       "138002     553464    Samsung  35283806     GT-I8262   GALAXY CORE GT-I8262   \n",
       "138003     553464      Redmi  86964104    M1908C3JG           Redmi Note 8   \n",
       "138004     553464      Redmi  86094104     M1901F7G            Redmi Note7   \n",
       "\n",
       "                         item_vendor  order_qty  unit_price  \n",
       "138000                 Samsung Korea         12        16.5  \n",
       "138001                 Samsung Korea         12        14.5  \n",
       "138002                 Samsung Korea          4        32.9  \n",
       "138003  Xiaomi Communications Co Ltd         12        81.5  \n",
       "138004  Xiaomi Communications Co Ltd         48         8.5  \n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the construction of a boolean vector maps directly to an elasticsearch query\n",
    "print(ed_invoices['country_name'] == 'Morocco')\n",
    "ed_invoices[(ed_invoices['country_name'] == 'Morocco')].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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