diff --git a/docs/sphinx/examples/online_retail_analysis.ipynb b/docs/sphinx/examples/online_retail_analysis.ipynb index f1ec185..b3821c2 100644 --- a/docs/sphinx/examples/online_retail_analysis.ipynb +++ b/docs/sphinx/examples/online_retail_analysis.ipynb @@ -1,1635 +1,2307 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:06.764412Z", - "iopub.status.busy": "2021-12-15T20:25:06.755567Z", - "iopub.status.idle": "2021-12-15T20:25:07.316950Z", - "shell.execute_reply": "2021-12-15T20:25:07.316561Z" - } - }, - "outputs": [], - "source": [ - "import eland as ed\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Fix console size for consistent test results\n", - "from eland.conftest import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Online Retail Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting Started\n", - "\n", - "To get started, let's create an `eland.DataFrame` by reading a csv file. This creates and populates the \n", - "`online-retail` index in the local Elasticsearch cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:07.324283Z", - "iopub.status.busy": "2021-12-15T20:25:07.323764Z", - "iopub.status.idle": "2021-12-15T20:25:16.241379Z", - "shell.execute_reply": "2021-12-15T20:25:16.241877Z" - } - }, - "outputs": [], - "source": [ - "df = ed.csv_to_eland(\"data/online-retail.csv.gz\",\n", - " es_client='http://localhost:9200', \n", - " es_dest_index='online-retail', \n", - " es_if_exists='replace', \n", - " es_dropna=True,\n", - " es_refresh=True,\n", - " compression='gzip',\n", - " index_col=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we see that the `\"_id\"` field was used to index our data frame. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:16.246737Z", - "iopub.status.busy": "2021-12-15T20:25:16.244084Z", - "iopub.status.idle": "2021-12-15T20:25:16.250080Z", - "shell.execute_reply": "2021-12-15T20:25:16.250410Z" - } - }, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "'_id'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.index.es_index_field" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we can check which field from elasticsearch are available to our eland data frame. `columns` is available as a parameter when instantiating the data frame which allows one to choose only a subset of fields from your index to be included in the data frame. Since we didn't set this parameter, we have access to all fields." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:16.254703Z", - "iopub.status.busy": "2021-12-15T20:25:16.254060Z", - "iopub.status.idle": "2021-12-15T20:25:16.256567Z", - "shell.execute_reply": "2021-12-15T20:25:16.256138Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode',\n", - " 'UnitPrice'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's see the data types of our fields. Running `df.dtypes`, we can see that elasticsearch field types are mapped to pandas field types." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:16.261335Z", - "iopub.status.busy": "2021-12-15T20:25:16.260762Z", - "iopub.status.idle": "2021-12-15T20:25:16.263024Z", - "shell.execute_reply": "2021-12-15T20:25:16.263323Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Country object\n", - "CustomerID float64\n", - "Description object\n", - "InvoiceDate object\n", - "InvoiceNo object\n", - "Quantity int64\n", - "StockCode object\n", - "UnitPrice float64\n", - "dtype: object" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also offer a `.es_info()` data frame method that shows all info about the underlying index. It also contains information about operations being passed from data frame methods to elasticsearch. More on this later." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:16.266245Z", - "iopub.status.busy": "2021-12-15T20:25:16.265860Z", - "iopub.status.idle": "2021-12-15T20:25:16.271135Z", - "shell.execute_reply": "2021-12-15T20:25:16.270816Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", - "CustomerID CustomerID True double None float64 True True False CustomerID\n", - "Description Description True keyword None object True True False Description\n", - "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", - "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", - "Quantity Quantity True long None int64 True True False Quantity\n", - "StockCode StockCode True keyword None object True True False StockCode\n", - "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", - "Operations:\n", - " tasks: []\n", - " size: None\n", - " sort_params: None\n", - " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", - " body: {}\n", - " post_processing: []\n", - "\n" - ] - } - ], - "source": [ - "print(df.es_info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Selecting and Indexing Data\n", - "\n", - "Now that we understand how to create a data frame and get access to it's underlying attributes, let's see how we can select subsets of our data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### head and tail\n", - "\n", - "much like pandas, eland data frames offer `.head(n)` and `.tail(n)` methods that return the first and last n rows, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:16.274779Z", - "iopub.status.busy": "2021-12-15T20:25:16.274393Z", - "iopub.status.idle": "2021-12-15T20:25:17.555325Z", - "shell.execute_reply": "2021-12-15T20:25:17.555642Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" + "cell_type": "markdown", + "source": [ + "# Online retail analysis using Eland\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elastic/eland/blob/main/docs/sphinx/examples/online_retail_analysis.ipynb)\n", + "\n", + "Learn how to analyze some online retail data using Eland." ], - "text/plain": [ - " Country CustomerID ... StockCode UnitPrice\n", - "0 United Kingdom 17850.0 ... 85123A 2.55\n", - "1 United Kingdom 17850.0 ... 71053 3.39\n", - "\n", - "[2 rows x 8 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:17.559534Z", - "iopub.status.busy": "2021-12-15T20:25:17.559123Z", - "iopub.status.idle": "2021-12-15T20:25:17.637500Z", - "shell.execute_reply": "2021-12-15T20:25:17.637125Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", - "CustomerID CustomerID True double None float64 True True False CustomerID\n", - "Description Description True keyword None object True True False Description\n", - "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", - "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", - "Quantity Quantity True long None int64 True True False Quantity\n", - "StockCode StockCode True keyword None object True True False StockCode\n", - "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", - "Operations:\n", - " tasks: [('tail': ('sort_field': '_doc', 'count': 2)), ('head': ('sort_field': '_doc', 'count': 2)), ('tail': ('sort_field': '_doc', 'count': 2))]\n", - " size: 2\n", - " sort_params: {'_doc': 'desc'}\n", - " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", - " body: {}\n", - " post_processing: [('sort_index'), ('head': ('count': 2)), ('tail': ('count': 2))]\n", - "\n" - ] - } - ], - "source": [ - "print(df.tail(2).head(2).tail(2).es_info())" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:17.640519Z", - "iopub.status.busy": "2021-12-15T20:25:17.640139Z", - "iopub.status.idle": "2021-12-15T20:25:18.647340Z", - "shell.execute_reply": "2021-12-15T20:25:18.646548Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Country CustomerID ... StockCode UnitPrice\n", - "12498 United Kingdom 16710.0 ... 20975 0.65\n", - "12499 United Kingdom 16710.0 ... 22445 2.95\n", - "\n", - "[2 rows x 8 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.tail(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Selecting columns\n", - "\n", - "you can also pass a list of columns to select columns from the data frame in a specified order." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:18.654238Z", - "iopub.status.busy": "2021-12-15T20:25:18.653517Z", - "iopub.status.idle": "2021-12-15T20:25:19.431749Z", - "shell.execute_reply": "2021-12-15T20:25:19.431127Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryInvoiceDate
0United Kingdom2010-12-01 08:26:00
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" - ], - "text/plain": [ - " Country InvoiceDate\n", - "0 United Kingdom 2010-12-01 08:26:00\n", - "1 United Kingdom 2010-12-01 08:26:00\n", - "2 United Kingdom 2010-12-01 08:26:00\n", - "3 United Kingdom 2010-12-01 08:26:00\n", - "4 United Kingdom 2010-12-01 08:26:00\n", - "\n", - "[5 rows x 2 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['Country', 'InvoiceDate']].head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Boolean Indexing\n", - "\n", - "we also allow you to filter the data frame using boolean indexing. Under the hood, a boolean index maps to a `terms` query that is then passed to elasticsearch to filter the index." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:19.440640Z", - "iopub.status.busy": "2021-12-15T20:25:19.439831Z", - "iopub.status.idle": "2021-12-15T20:25:20.066747Z", - "shell.execute_reply": "2021-12-15T20:25:20.067477Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'term': {'Country': 'Germany'}}\n" - ] + "metadata": { + "id": "2IM_dmhFeRhR" + } }, { - "data": { - "text/html": [ - "
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" + "cell_type": "markdown", + "source": [ + "## Install and import packages" ], - "text/plain": [ - " Country CustomerID ... StockCode UnitPrice\n", - "1109 Germany 12662.0 ... 22809 2.95\n", - "1110 Germany 12662.0 ... 84347 2.55\n", - "1111 Germany 12662.0 ... 84945 0.85\n", - "1112 Germany 12662.0 ... 22242 1.65\n", - "1113 Germany 12662.0 ... 22244 1.95\n", - "\n", - "[5 rows x 8 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the construction of a boolean vector maps directly to an elasticsearch query\n", - "print(df['Country']=='Germany')\n", - "df[(df['Country']=='Germany')].head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can also filter the data frame using a list of values." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:20.077022Z", - "iopub.status.busy": "2021-12-15T20:25:20.076412Z", - "iopub.status.idle": "2021-12-15T20:25:21.233013Z", - "shell.execute_reply": "2021-12-15T20:25:21.234073Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'terms': {'Country': ['Germany', 'United States']}}\n" - ] + "metadata": { + "id": "xx09MAYFe3Ep" + } }, { - "data": { - "text/html": [ - "
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" + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:06.764412Z", + "iopub.status.busy": "2021-12-15T20:25:06.755567Z", + "iopub.status.idle": "2021-12-15T20:25:07.316950Z", + "shell.execute_reply": "2021-12-15T20:25:07.316561Z" + }, + "id": "Z7MEVJy9W_sd", + "outputId": "91b215ff-9f4f-4b1f-d204-f24bcc38b3f6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting eland\n", + " Downloading eland-8.11.1-py3-none-any.whl (157 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.9/157.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting elasticsearch<9,>=8.3 (from eland)\n", + " Downloading elasticsearch-8.11.1-py3-none-any.whl (412 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m412.8/412.8 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pandas<2,>=1.5 in /usr/local/lib/python3.10/dist-packages (from eland) (1.5.3)\n", + "Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.10/dist-packages (from eland) (3.7.1)\n", + "Requirement already satisfied: numpy<2,>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from eland) (1.23.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from eland) (23.2)\n", + "Collecting elastic-transport<9,>=8 (from elasticsearch<9,>=8.3->eland)\n", + " Downloading elastic_transport-8.11.0-py3-none-any.whl (59 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.8/59.8 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (4.47.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (1.4.5)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (9.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2,>=1.5->eland) (2023.3.post1)\n", + "Requirement already satisfied: urllib3<3,>=1.26.2 in /usr/local/lib/python3.10/dist-packages (from elastic-transport<9,>=8->elasticsearch<9,>=8.3->eland) (2.0.7)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from elastic-transport<9,>=8->elasticsearch<9,>=8.3->eland) (2023.11.17)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6->eland) (1.16.0)\n", + "Installing collected packages: elastic-transport, elasticsearch, eland\n", + "Successfully installed eland-8.11.1 elastic-transport-8.11.0 elasticsearch-8.11.1\n", + "Requirement already satisfied: elasticsearch in /usr/local/lib/python3.10/dist-packages (8.11.1)\n", + "Requirement already satisfied: elastic-transport<9,>=8 in /usr/local/lib/python3.10/dist-packages (from elasticsearch) (8.11.0)\n", + "Requirement already satisfied: urllib3<3,>=1.26.2 in /usr/local/lib/python3.10/dist-packages (from elastic-transport<9,>=8->elasticsearch) (2.0.7)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from elastic-transport<9,>=8->elasticsearch) (2023.11.17)\n" + ] + } ], - "text/plain": [ - " Country CustomerID ... StockCode UnitPrice\n", - "0 United Kingdom 17850.0 ... 85123A 2.55\n", - "1 United Kingdom 17850.0 ... 71053 3.39\n", - "2 United Kingdom 17850.0 ... 84406B 2.75\n", - "3 United Kingdom 17850.0 ... 84029G 3.39\n", - "4 United Kingdom 17850.0 ... 84029E 3.39\n", - "\n", - "[5 rows x 8 columns]" + "source": [ + "!pip install eland\n", + "!pip install elasticsearch\n", + "\n", + "from elasticsearch import Elasticsearch\n", + "import eland as ed\n", + "import getpass\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Fix console size for consistent test results\n", + "from eland.conftest import *" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(df['Country'].isin(['Germany', 'United States']))\n", - "df[df['Country'].isin(['Germany', 'United Kingdom'])].head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also combine boolean vectors to further filter the data frame." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:21.245390Z", - "iopub.status.busy": "2021-12-15T20:25:21.244737Z", - "iopub.status.idle": "2021-12-15T20:25:22.358701Z", - "shell.execute_reply": "2021-12-15T20:25:22.355150Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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CountryCustomerID...StockCodeUnitPrice
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [Country, CustomerID, Description, InvoiceDate, InvoiceNo, Quantity, StockCode, UnitPrice]\n", - "Index: []\n", - "\n", - "[0 rows x 8 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df['Country']=='Germany') & (df['Quantity']>90)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using this example, let see how eland translates this boolean filter to an elasticsearch `bool` query." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:22.383610Z", - "iopub.status.busy": "2021-12-15T20:25:22.370577Z", - "iopub.status.idle": "2021-12-15T20:25:22.390275Z", - "shell.execute_reply": "2021-12-15T20:25:22.388963Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", - "CustomerID CustomerID True double None float64 True True False CustomerID\n", - "Description Description True keyword None object True True False Description\n", - "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", - "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", - "Quantity Quantity True long None int64 True True False Quantity\n", - "StockCode StockCode True keyword None object True True False StockCode\n", - "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", - "Operations:\n", - " tasks: [('boolean_filter': ('boolean_filter': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}))]\n", - " size: None\n", - " sort_params: None\n", - " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", - " body: {'query': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}}\n", - " post_processing: []\n", - "\n" - ] - } - ], - "source": [ - "print(df[(df['Country']=='Germany') & (df['Quantity']>90)].es_info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregation and Descriptive Statistics\n", - "\n", - "Let's begin to ask some questions of our data and use eland to get the answers." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**How many different countries are there?**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:22.398231Z", - "iopub.status.busy": "2021-12-15T20:25:22.397459Z", - "iopub.status.idle": "2021-12-15T20:25:22.482238Z", - "shell.execute_reply": "2021-12-15T20:25:22.481338Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "16" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Country'].nunique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**What is the total sum of products ordered?**" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:22.492668Z", - "iopub.status.busy": "2021-12-15T20:25:22.491590Z", - "iopub.status.idle": "2021-12-15T20:25:22.580015Z", - "shell.execute_reply": "2021-12-15T20:25:22.578300Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "111960" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Quantity'].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Show me the sum, mean, min, and max of the qunatity and unit_price fields**" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:22.601432Z", - "iopub.status.busy": "2021-12-15T20:25:22.600117Z", - "iopub.status.idle": "2021-12-15T20:25:22.702450Z", - "shell.execute_reply": "2021-12-15T20:25:22.701499Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " CustomerID Quantity UnitPrice\n", - "count 10729.000000 15000.000000 15000.000000\n", - "mean 15590.776680 7.464000 4.103233\n", - "std 1764.189592 85.930116 20.106214\n", - "min 12347.000000 -9360.000000 0.000000\n", - "25% 14222.249164 1.000000 1.250000\n", - "50% 15663.037856 2.000000 2.510000\n", - "75% 17219.040670 6.425347 4.210000\n", - "max 18239.000000 2880.000000 950.990000" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_IGNORE_OUTPUT\n", - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Show me a histogram of numeric columns**" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:23.000466Z", - "iopub.status.busy": "2021-12-15T20:25:22.999571Z", - "iopub.status.idle": "2021-12-15T20:25:23.576387Z", - "shell.execute_reply": "2021-12-15T20:25:23.576703Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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"execution": { - "iopub.execute_input": "2021-12-15T20:25:26.483774Z", - "iopub.status.busy": "2021-12-15T20:25:26.482084Z", - "iopub.status.idle": "2021-12-15T20:25:26.907406Z", - "shell.execute_reply": "2021-12-15T20:25:26.906448Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 6\n", - "1 6\n", - "2 8\n", - "3 6\n", - "4 6\n", - "Name: Quantity, dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Quantity'].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:26.912916Z", - "iopub.status.busy": "2021-12-15T20:25:26.910149Z", - "iopub.status.idle": "2021-12-15T20:25:27.361783Z", - "shell.execute_reply": "2021-12-15T20:25:27.362723Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 2.55\n", - "1 3.39\n", - "2 2.75\n", - "3 3.39\n", - "4 3.39\n", - "Name: UnitPrice, dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['UnitPrice'].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:27.383414Z", - "iopub.status.busy": "2021-12-15T20:25:27.374098Z", - "iopub.status.idle": "2021-12-15T20:25:27.387546Z", - "shell.execute_reply": "2021-12-15T20:25:27.388753Z" - } - }, - "outputs": [], - "source": [ - "product = df['Quantity'] * df['UnitPrice']" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:27.398754Z", - "iopub.status.busy": "2021-12-15T20:25:27.397557Z", - "iopub.status.idle": "2021-12-15T20:25:27.818022Z", - "shell.execute_reply": "2021-12-15T20:25:27.819640Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 15.30\n", - "1 20.34\n", - "2 22.00\n", - "3 20.34\n", - "4 20.34\n", - "dtype: float64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "product.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "String concatenation" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-15T20:25:27.837007Z", - "iopub.status.busy": "2021-12-15T20:25:27.836370Z", - "iopub.status.idle": "2021-12-15T20:25:29.072872Z", - "shell.execute_reply": "2021-12-15T20:25:29.074153Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 United Kingdom85123A\n", - "1 United Kingdom71053\n", - "2 United Kingdom84406B\n", - "3 United Kingdom84029G\n", - "4 United Kingdom84029E\n", - " ... \n", - "12495 United Kingdom84692\n", - "12496 United Kingdom22075\n", - "12497 United Kingdom20979\n", - "12498 United Kingdom20975\n", - "12499 United Kingdom22445\n", - "Length: 15000, dtype: object" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Country'] + df['StockCode']" - ] - } - ], - "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": "3.8.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "metadata": { - "collapsed": false }, - "source": [] - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + { + "cell_type": "markdown", + "source": [ + "## Connect to Elasticsearch\n", + "\n", + "First we need to connect to a running Elasticsearch instance.\n", + "In this example we'll be using Elastic Cloud.\n", + "Sign up for a [free trial](https://cloud.elastic.co/registration).\n", + "\n", + "See [documentation](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/connecting.html#connect-self-managed-new) if you want to connect to a self-managed cluster." + ], + "metadata": { + "id": "SD0Ul-I_Xipy" + } + }, + { + "cell_type": "code", + "source": [ + "# Connect to an Elastic Cloud instance\n", + "\n", + "ELASTIC_CLOUD_ID = getpass.getpass(\"Cloud ID:\")\n", + "ELASTIC_CLOUD_PASSWORD = getpass.getpass(\"`elastic` user password:\")\n", + "\n", + "es = Elasticsearch(\n", + " cloud_id=ELASTIC_CLOUD_ID,\n", + " basic_auth=(\"elastic\", ELASTIC_CLOUD_PASSWORD)\n", + ")\n", + "print(es.info())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mNrJvFU1Yagd", + "outputId": "db8829bf-bd59-4d86-8b73-c956d09f373e" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloud ID:··········\n", + "`elastic` user password:··········\n", + "{'name': 'instance-0000000001', 'cluster_name': '69662f53fe844e2d81effcbc7f41e867', 'cluster_uuid': 'GHyCC4NpTAC3SyxZkx65Jw', 'version': {'number': '8.12.0-SNAPSHOT', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '38ddf39a3efc422a702adc83b1bb2cd6fc2edc5b', 'build_date': '2024-01-03T12:58:40.771552945Z', 'build_snapshot': True, 'lucene_version': '9.9.1', 'minimum_wire_compatibility_version': '7.17.0', 'minimum_index_compatibility_version': '7.0.0'}, 'tagline': 'You Know, for Search'}\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uvhOwB3dW_sg" + }, + "source": [ + "## Download test data\n", + "\n", + "Let's start by downloading our test data." + ] + }, + { + "cell_type": "code", + "source": [ + "import requests\n", + "\n", + "# URL of the raw file on GitHub\n", + "file_url = \"https://github.com/elastic/eland/raw/main/docs/sphinx/examples/data/online-retail.csv.gz\"\n", + "\n", + "# Local path where you want to save the file\n", + "local_filename = \"online-retail.csv.gz\"\n", + "\n", + "# Send a GET request to the file URL\n", + "response = requests.get(file_url, stream=True)\n", + "\n", + "# Open a local file in binary write mode\n", + "with open(local_filename, 'wb') as file:\n", + " for chunk in response.iter_content(chunk_size=128):\n", + " file.write(chunk)\n", + "\n", + "print(f\"File downloaded: {local_filename}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FTCeS2rFZrYh", + "outputId": "131aeeac-0af5-4746-e639-626b3c80fb69" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "File downloaded: online-retail.csv.gz\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Create Eland dataframe\n", + "\n", + "To get started, let's create an `eland.DataFrame` by reading a csv file. This creates and populates the\n", + "`online-retail` index in the local Elasticsearch cluster." + ], + "metadata": { + "id": "V-BiYFnvZwTd" + } + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:07.324283Z", + "iopub.status.busy": "2021-12-15T20:25:07.323764Z", + "iopub.status.idle": "2021-12-15T20:25:16.241379Z", + "shell.execute_reply": "2021-12-15T20:25:16.241877Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3Pq2jtVQW_sh", + "outputId": "e677a2eb-e59d-4403-f19b-7326ec954a6b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/eland/etl.py:529: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n", + " reader = pd.read_csv(filepath_or_buffer, **kwargs)\n", + "/usr/local/lib/python3.10/dist-packages/eland/etl.py:529: FutureWarning: The squeeze argument has been deprecated and will be removed in a future version. Append .squeeze(\"columns\") to the call to squeeze.\n", + "\n", + "\n", + " reader = pd.read_csv(filepath_or_buffer, **kwargs)\n" + ] + } + ], + "source": [ + "df = ed.csv_to_eland(\"online-retail.csv.gz\",\n", + " es_client=es,\n", + " es_dest_index='online-retail',\n", + " es_if_exists='replace',\n", + " es_dropna=True,\n", + " es_refresh=True,\n", + " compression='gzip',\n", + " index_col=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RT8kO2rqW_sh" + }, + "source": [ + "Here we see that the `\"_id\"` field was used to index our data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:16.246737Z", + "iopub.status.busy": "2021-12-15T20:25:16.244084Z", + "iopub.status.idle": "2021-12-15T20:25:16.250080Z", + "shell.execute_reply": "2021-12-15T20:25:16.250410Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "x2um6Xd4W_sh", + "outputId": "69a5a3b3-95cb-4851-dec5-f54e55d16235" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'_id'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "df.index.es_index_field" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YHFiR_8MW_si" + }, + "source": [ + "Next, we can check which field from elasticsearch are available to our eland data frame. `columns` is available as a parameter when instantiating the data frame which allows one to choose only a subset of fields from your index to be included in the data frame. Since we didn't set this parameter, we have access to all fields." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:16.254703Z", + "iopub.status.busy": "2021-12-15T20:25:16.254060Z", + "iopub.status.idle": "2021-12-15T20:25:16.256567Z", + "shell.execute_reply": "2021-12-15T20:25:16.256138Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h_6_2YphW_si", + "outputId": "1fb83e3b-8827-4f18-db7d-79bb25e7123d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode',\n", + " 'UnitPrice'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-LmvXDA3W_si" + }, + "source": [ + "Now, let's see the data types of our fields. Running `df.dtypes`, we can see that elasticsearch field types are mapped to pandas field types." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:16.261335Z", + "iopub.status.busy": "2021-12-15T20:25:16.260762Z", + "iopub.status.idle": "2021-12-15T20:25:16.263024Z", + "shell.execute_reply": "2021-12-15T20:25:16.263323Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4KEZ3zsEW_si", + "outputId": "090674df-7f4d-41bd-b4bf-3ee476945775" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Country object\n", + "CustomerID float64\n", + "Description object\n", + "InvoiceDate object\n", + "InvoiceNo object\n", + "Quantity int64\n", + "StockCode object\n", + "UnitPrice float64\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Levcmf0W_si" + }, + "source": [ + "We also offer a `.es_info()` data frame method that shows all info about the underlying index. It also contains information about operations being passed from data frame methods to elasticsearch. More on this later." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:16.266245Z", + "iopub.status.busy": "2021-12-15T20:25:16.265860Z", + "iopub.status.idle": "2021-12-15T20:25:16.271135Z", + "shell.execute_reply": "2021-12-15T20:25:16.270816Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RhxvDBAiW_si", + "outputId": "db60167f-d973-41d9-bd42-438855cef1a4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", + "CustomerID CustomerID True double None float64 True True False CustomerID\n", + "Description Description True keyword None object True True False Description\n", + "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", + "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", + "Quantity Quantity True long None int64 True True False Quantity\n", + "StockCode StockCode True keyword None object True True False StockCode\n", + "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", + "Operations:\n", + " tasks: []\n", + " size: None\n", + " sort_params: None\n", + " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", + " body: {}\n", + " post_processing: []\n", + "\n" + ] + } + ], + "source": [ + "print(df.es_info())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zlp7YIrPW_sj" + }, + "source": [ + "## Selecting and Indexing Data\n", + "\n", + "Now that we understand how to create a data frame and get access to it's underlying attributes, let's see how we can select subsets of our data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6dvAlxBTW_sj" + }, + "source": [ + "### head and tail\n", + "\n", + "much like pandas, eland data frames offer `.head(n)` and `.tail(n)` methods that return the first and last n rows, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:16.274779Z", + "iopub.status.busy": "2021-12-15T20:25:16.274393Z", + "iopub.status.idle": "2021-12-15T20:25:17.555325Z", + "shell.execute_reply": "2021-12-15T20:25:17.555642Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "id": "WaV6ZJ7CW_sj", + "outputId": "203a8b15-5a08-4808-a675-8a98d1174c1e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Country CustomerID ... 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" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:17.559534Z", + "iopub.status.busy": "2021-12-15T20:25:17.559123Z", + "iopub.status.idle": "2021-12-15T20:25:17.637500Z", + "shell.execute_reply": "2021-12-15T20:25:17.637125Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XpwHbrGwW_sj", + "outputId": "130ba338-abcf-4fe4-cce3-a1840f0fe46b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", + "CustomerID CustomerID True double None float64 True True False CustomerID\n", + "Description Description True keyword None object True True False Description\n", + "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", + "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", + "Quantity Quantity True long None int64 True True False Quantity\n", + "StockCode StockCode True keyword None object True True False StockCode\n", + "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", + "Operations:\n", + " tasks: [('tail': ('sort_field': '_doc', 'count': 2)), ('head': ('sort_field': '_doc', 'count': 2)), ('tail': ('sort_field': '_doc', 'count': 2))]\n", + " size: 2\n", + " sort_params: {'_doc': 'desc'}\n", + " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", + " body: {}\n", + " post_processing: [('sort_index'), ('head': ('count': 2)), ('tail': ('count': 2))]\n", + "\n" + ] + } + ], + "source": [ + "print(df.tail(2).head(2).tail(2).es_info())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:17.640519Z", + "iopub.status.busy": "2021-12-15T20:25:17.640139Z", + "iopub.status.idle": "2021-12-15T20:25:18.647340Z", + "shell.execute_reply": "2021-12-15T20:25:18.646548Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "id": "Zf-TFwvXW_sj", + "outputId": "4300d820-843d-448f-879d-586756c2e620" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Country CustomerID ... StockCode UnitPrice\n", + "14998 United Kingdom 17419.0 ... 21773 1.25\n", + "14999 United Kingdom 17419.0 ... 22149 2.10\n", + "\n", + "[2 rows x 8 columns]" + ], + "text/html": [ + "
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14998United Kingdom17419.0...217731.25
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" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ], + "source": [ + "df.tail(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AkbC-qckW_sj" + }, + "source": [ + "### Selecting columns\n", + "\n", + "you can also pass a list of columns to select columns from the data frame in a specified order." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:18.654238Z", + "iopub.status.busy": "2021-12-15T20:25:18.653517Z", + "iopub.status.idle": "2021-12-15T20:25:19.431749Z", + "shell.execute_reply": "2021-12-15T20:25:19.431127Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "id": "Tdhy8cQzW_sk", + "outputId": "ab4bcc6b-43ab-45b3-8880-545d52410851" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Country InvoiceDate\n", + "0 United Kingdom 2010-12-01 08:26:00\n", + "1 United Kingdom 2010-12-01 08:26:00\n", + "2 United Kingdom 2010-12-01 08:26:00\n", + "3 United Kingdom 2010-12-01 08:26:00\n", + "4 United Kingdom 2010-12-01 08:26:00\n", + "\n", + "[5 rows x 2 columns]" + ], + "text/html": [ + "
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3United Kingdom2010-12-01 08:26:00
4United Kingdom2010-12-01 08:26:00
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5 rows × 2 columns

" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "df[['Country', 'InvoiceDate']].head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CYV4WfSHW_sk" + }, + "source": [ + "### Boolean Indexing\n", + "\n", + "we also allow you to filter the data frame using boolean indexing. Under the hood, a boolean index maps to a `terms` query that is then passed to elasticsearch to filter the index." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:19.440640Z", + "iopub.status.busy": "2021-12-15T20:25:19.439831Z", + "iopub.status.idle": "2021-12-15T20:25:20.066747Z", + "shell.execute_reply": "2021-12-15T20:25:20.067477Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250 + }, + "id": "p2SPKSOEW_sk", + "outputId": "288a036f-fda0-4d22-9fb4-15ba2cb551b3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'term': {'Country': 'Germany'}}\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Country CustomerID ... StockCode UnitPrice\n", + "1109 Germany 12662.0 ... 22809 2.95\n", + "1110 Germany 12662.0 ... 84347 2.55\n", + "1111 Germany 12662.0 ... 84945 0.85\n", + "1112 Germany 12662.0 ... 22242 1.65\n", + "1113 Germany 12662.0 ... 22244 1.95\n", + "\n", + "[5 rows x 8 columns]" + ], + "text/html": [ + "
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CountryCustomerID...StockCodeUnitPrice
1109Germany12662.0...228092.95
1110Germany12662.0...843472.55
1111Germany12662.0...849450.85
1112Germany12662.0...222421.65
1113Germany12662.0...222441.95
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5 rows × 8 columns

" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "# the construction of a boolean vector maps directly to an elasticsearch query\n", + "print(df['Country']=='Germany')\n", + "df[(df['Country']=='Germany')].head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xV2bkyC3W_sk" + }, + "source": [ + "we can also filter the data frame using a list of values." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:20.077022Z", + "iopub.status.busy": "2021-12-15T20:25:20.076412Z", + "iopub.status.idle": "2021-12-15T20:25:21.233013Z", + "shell.execute_reply": "2021-12-15T20:25:21.234073Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250 + }, + "id": "atifgs9hW_sk", + "outputId": "26468789-6032-428c-f64a-ce36c5a5f426" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'terms': {'Country': ['Germany', 'United States']}}\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Country CustomerID ... StockCode UnitPrice\n", + "0 United Kingdom 17850.0 ... 85123A 2.55\n", + "1 United Kingdom 17850.0 ... 71053 3.39\n", + "2 United Kingdom 17850.0 ... 84406B 2.75\n", + "3 United Kingdom 17850.0 ... 84029G 3.39\n", + "4 United Kingdom 17850.0 ... 84029E 3.39\n", + "\n", + "[5 rows x 8 columns]" + ], + "text/html": [ + "
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3United Kingdom17850.0...84029G3.39
4United Kingdom17850.0...84029E3.39
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5 rows × 8 columns

" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "print(df['Country'].isin(['Germany', 'United States']))\n", + "df[df['Country'].isin(['Germany', 'United Kingdom'])].head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oFwNF24EW_sk" + }, + "source": [ + "We can also combine boolean vectors to further filter the data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:21.245390Z", + "iopub.status.busy": "2021-12-15T20:25:21.244737Z", + "iopub.status.idle": "2021-12-15T20:25:22.358701Z", + "shell.execute_reply": "2021-12-15T20:25:22.355150Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 78 + }, + "id": "WK3xl6JQW_sk", + "outputId": "7e7d9cb2-79d8-4f2b-bb95-02a316246fa6" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Country, CustomerID, Description, InvoiceDate, InvoiceNo, Quantity, StockCode, UnitPrice]\n", + "Index: []\n", + "\n", + "[0 rows x 8 columns]" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "df[(df['Country']=='Germany') & (df['Quantity']>90)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OOZMEOTXW_sk" + }, + "source": [ + "Using this example, let see how eland translates this boolean filter to an elasticsearch `bool` query." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:22.383610Z", + "iopub.status.busy": "2021-12-15T20:25:22.370577Z", + "iopub.status.idle": "2021-12-15T20:25:22.390275Z", + "shell.execute_reply": "2021-12-15T20:25:22.388963Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zg5CX5A9W_sl", + "outputId": "929c23f7-be9b-4ea6-c1bc-e934f25e9fb0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "es_index_pattern: online-retail\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 Country True keyword None object True True False Country\n", + "CustomerID CustomerID True double None float64 True True False CustomerID\n", + "Description Description True keyword None object True True False Description\n", + "InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n", + "InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n", + "Quantity Quantity True long None int64 True True False Quantity\n", + "StockCode StockCode True keyword None object True True False StockCode\n", + "UnitPrice UnitPrice True double None float64 True True False UnitPrice\n", + "Operations:\n", + " tasks: [('boolean_filter': ('boolean_filter': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}))]\n", + " size: None\n", + " sort_params: None\n", + " _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n", + " body: {'query': {'bool': {'must': [{'term': {'Country': 'Germany'}}, {'range': {'Quantity': {'gt': 90}}}]}}}\n", + " post_processing: []\n", + "\n" + ] + } + ], + "source": [ + "print(df[(df['Country']=='Germany') & (df['Quantity']>90)].es_info())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QRTjR8d3W_sl" + }, + "source": [ + "## Aggregation and Descriptive Statistics\n", + "\n", + "Let's begin to ask some questions of our data and use eland to get the answers." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pc3OGsQ5W_sl" + }, + "source": [ + "**How many different countries are there?**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:22.398231Z", + "iopub.status.busy": "2021-12-15T20:25:22.397459Z", + "iopub.status.idle": "2021-12-15T20:25:22.482238Z", + "shell.execute_reply": "2021-12-15T20:25:22.481338Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9p8JrleuW_sl", + "outputId": "9323b109-a59a-4b7c-f6c4-14996666591d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "16" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "df['Country'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9w_Ge7ESW_sl" + }, + "source": [ + "**What is the total sum of products ordered?**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:22.492668Z", + "iopub.status.busy": "2021-12-15T20:25:22.491590Z", + "iopub.status.idle": "2021-12-15T20:25:22.580015Z", + "shell.execute_reply": "2021-12-15T20:25:22.578300Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MFu01N3LW_sl", + "outputId": "0e10430a-2a2d-4f4c-be41-01f0bcb9afde" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "111960" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "df['Quantity'].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Nwc-ybwW_sl" + }, + "source": [ + "**Show me the sum, mean, min, and max of the qunatity and unit_price fields**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:22.601432Z", + "iopub.status.busy": "2021-12-15T20:25:22.600117Z", + "iopub.status.idle": "2021-12-15T20:25:22.702450Z", + "shell.execute_reply": "2021-12-15T20:25:22.701499Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "id": "QhqUeIQbW_sl", + "outputId": "aeec1b5e-bee4-4cf5-e338-def0d82b0451" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Quantity 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\n" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "# NBVAL_IGNORE_OUTPUT\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LfPjHhpKW_sn" + }, + "source": [ + "**Show me a histogram of numeric columns**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:23.000466Z", + "iopub.status.busy": "2021-12-15T20:25:22.999571Z", + "iopub.status.idle": "2021-12-15T20:25:23.576387Z", + "shell.execute_reply": "2021-12-15T20:25:23.576703Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "ZJ1VoTG_W_sn", + "outputId": "a32d2fec-17db-428a-d4a2-71a17faa41fa" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "df[(df['Quantity']>-50) &\n", + " (df['Quantity']<50) &\n", + " (df['UnitPrice']>0) &\n", + " (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:23.584264Z", + "iopub.status.busy": "2021-12-15T20:25:23.583784Z", + "iopub.status.idle": "2021-12-15T20:25:24.494000Z", + "shell.execute_reply": "2021-12-15T20:25:24.493618Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "JbSWJJKUW_sn", + "outputId": "94aaf248-35ea-4e1d-fbf8-a1cf60b7778d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "df[(df['Quantity']>-50) &\n", + " (df['Quantity']<50) &\n", + " (df['UnitPrice']>0) &\n", + " (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30, log=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:24.504460Z", + "iopub.status.busy": "2021-12-15T20:25:24.504086Z", + "iopub.status.idle": "2021-12-15T20:25:26.468550Z", + "shell.execute_reply": "2021-12-15T20:25:26.466711Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5QysMacBW_so", + "outputId": "e3356521-63c3-404c-e8db-5ed6a0407162" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Country CustomerID ... StockCode UnitPrice\n", + "46 United Kingdom 13748.0 ... 22086 2.55\n", + "83 United Kingdom 15291.0 ... 21733 2.55\n", + "96 United Kingdom 14688.0 ... 21212 0.42\n", + "102 United Kingdom 14688.0 ... 85071B 0.38\n", + "176 United Kingdom 16029.0 ... 85099C 1.65\n", + "... ... ... ... ... ...\n", + "14784 United Kingdom 15061.0 ... 22423 10.95\n", + "14785 United Kingdom 15061.0 ... 22075 1.45\n", + "14788 United Kingdom 15061.0 ... 17038 0.07\n", + "14974 United Kingdom 14739.0 ... 21704 0.72\n", + "14980 United Kingdom 14739.0 ... 22178 1.06\n", + "\n", + "[258 rows x 8 columns]\n" + ] + } + ], + "source": [ + "filter_quantity_price = df[(df['Quantity'] > 50) & (df['UnitPrice'] < 100)]\n", + "print(filter_quantity_price)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xG1_wb5RW_so" + }, + "source": [ + "## Arithmetic Operations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CxqA7gO-W_so" + }, + "source": [ + "Numeric values" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:26.483774Z", + "iopub.status.busy": "2021-12-15T20:25:26.482084Z", + "iopub.status.idle": "2021-12-15T20:25:26.907406Z", + "shell.execute_reply": "2021-12-15T20:25:26.906448Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HWoI0tYcW_so", + "outputId": "727db684-740b-45bf-9e4e-3887674b3e8c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 6\n", + "1 6\n", + "2 8\n", + "3 6\n", + "4 6\n", + "Name: Quantity, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ], + "source": [ + "df['Quantity'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:26.912916Z", + "iopub.status.busy": "2021-12-15T20:25:26.910149Z", + "iopub.status.idle": "2021-12-15T20:25:27.361783Z", + "shell.execute_reply": "2021-12-15T20:25:27.362723Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I6Rs0pTAW_sp", + "outputId": "68aa067b-b70c-4030-8acf-a2b36310fc52" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 2.55\n", + "1 3.39\n", + "2 2.75\n", + "3 3.39\n", + "4 3.39\n", + "Name: UnitPrice, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "source": [ + "df['UnitPrice'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:27.383414Z", + "iopub.status.busy": "2021-12-15T20:25:27.374098Z", + "iopub.status.idle": "2021-12-15T20:25:27.387546Z", + "shell.execute_reply": "2021-12-15T20:25:27.388753Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbibhbn6W_sp", + "outputId": "d0b6b88b-6282-4901-fea1-94d2e94c773d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/eland/field_mappings.py:715: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self._mappings_capabilities = self._mappings_capabilities.append(\n" + ] + } + ], + "source": [ + "product = df['Quantity'] * df['UnitPrice']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:27.398754Z", + "iopub.status.busy": "2021-12-15T20:25:27.397557Z", + "iopub.status.idle": "2021-12-15T20:25:27.818022Z", + "shell.execute_reply": "2021-12-15T20:25:27.819640Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2FstW8ZCW_sp", + "outputId": "b55d687e-6b7d-48a8-8815-e64bc6ebf6dd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 15.30\n", + "1 20.34\n", + "2 22.00\n", + "3 20.34\n", + "4 20.34\n", + "dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 44 + } + ], + "source": [ + "product.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pd2zMWGBW_sp" + }, + "source": [ + "String concatenation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-15T20:25:27.837007Z", + "iopub.status.busy": "2021-12-15T20:25:27.836370Z", + "iopub.status.idle": "2021-12-15T20:25:29.072872Z", + "shell.execute_reply": "2021-12-15T20:25:29.074153Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5MpSfh0fW_sp", + "outputId": "e0c52ddf-19bc-40af-813a-75c7e808bef6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/eland/field_mappings.py:715: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self._mappings_capabilities = self._mappings_capabilities.append(\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 United Kingdom85123A\n", + "1 United Kingdom71053\n", + "2 United Kingdom84406B\n", + "3 United Kingdom84029G\n", + "4 United Kingdom84029E\n", + " ... \n", + "14995 United Kingdom72349B\n", + "14996 United Kingdom72741\n", + "14997 United Kingdom22762\n", + "14998 United Kingdom21773\n", + "14999 United Kingdom22149\n", + "Length: 15000, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "df['Country'] + df['StockCode']" + ] + } + ], + "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": "3.8.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file