mirror of
https://github.com/elastic/eland.git
synced 2025-07-24 00:00:39 +08:00
2307 lines
136 KiB
Plaintext
2307 lines
136 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Online retail analysis using Eland\n",
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"\n",
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"[](https://colab.research.google.com/github/elastic/eland/blob/main/docs/sphinx/examples/online_retail_analysis.ipynb)\n",
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||
"\n",
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"Learn how to analyze some online retail data using Eland."
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],
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||
"metadata": {
|
||
"id": "2IM_dmhFeRhR"
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||
}
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||
},
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||
{
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||
"cell_type": "markdown",
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"source": [
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"## Install and import packages"
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||
],
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"metadata": {
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"id": "xx09MAYFe3Ep"
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||
}
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2021-12-15T20:25:06.764412Z",
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||
"iopub.status.busy": "2021-12-15T20:25:06.755567Z",
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||
"iopub.status.idle": "2021-12-15T20:25:07.316950Z",
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"shell.execute_reply": "2021-12-15T20:25:07.316561Z"
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||
},
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"id": "Z7MEVJy9W_sd",
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||
"outputId": "91b215ff-9f4f-4b1f-d204-f24bcc38b3f6",
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"colab": {
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||
"base_uri": "https://localhost:8080/"
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||
}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting eland\n",
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" Downloading eland-8.11.1-py3-none-any.whl (157 kB)\n",
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"\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",
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"\u001b[?25hCollecting elasticsearch<9,>=8.3 (from eland)\n",
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" Downloading elasticsearch-8.11.1-py3-none-any.whl (412 kB)\n",
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"\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",
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"\u001b[?25hRequirement already satisfied: pandas<2,>=1.5 in /usr/local/lib/python3.10/dist-packages (from eland) (1.5.3)\n",
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"Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.10/dist-packages (from eland) (3.7.1)\n",
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"Requirement already satisfied: numpy<2,>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from eland) (1.23.5)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from eland) (23.2)\n",
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"Collecting elastic-transport<9,>=8 (from elasticsearch<9,>=8.3->eland)\n",
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" Downloading elastic_transport-8.11.0-py3-none-any.whl (59 kB)\n",
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"\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",
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"\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",
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"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (4.47.0)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (1.4.5)\n",
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||
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (9.4.0)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (3.1.1)\n",
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||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->eland) (2.8.2)\n",
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||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2,>=1.5->eland) (2023.3.post1)\n",
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||
"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",
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||
"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",
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||
"Installing collected packages: elastic-transport, elasticsearch, eland\n",
|
||
"Successfully installed eland-8.11.1 elastic-transport-8.11.0 elasticsearch-8.11.1\n",
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||
"Requirement already satisfied: elasticsearch in /usr/local/lib/python3.10/dist-packages (8.11.1)\n",
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||
"Requirement already satisfied: elastic-transport<9,>=8 in /usr/local/lib/python3.10/dist-packages (from elasticsearch) (8.11.0)\n",
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||
"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",
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||
"Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from elastic-transport<9,>=8->elasticsearch) (2023.11.17)\n"
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||
]
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||
}
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||
],
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"source": [
|
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"!pip install eland\n",
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"!pip install elasticsearch\n",
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"\n",
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"from elasticsearch import Elasticsearch\n",
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"import eland as ed\n",
|
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"import getpass\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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||
"# Fix console size for consistent test results\n",
|
||
"from eland.conftest import *"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"source": [
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"## Connect to Elasticsearch\n",
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||
"\n",
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"First we need to connect to a running Elasticsearch instance.\n",
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||
"In this example we'll be using Elastic Cloud.\n",
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"Sign up for a [free trial](https://cloud.elastic.co/registration).\n",
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"\n",
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"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."
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||
],
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||
"metadata": {
|
||
"id": "SD0Ul-I_Xipy"
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||
}
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||
},
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||
{
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||
"cell_type": "code",
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||
"source": [
|
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"# Connect to an Elastic Cloud instance\n",
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"\n",
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"ELASTIC_CLOUD_ID = getpass.getpass(\"Cloud ID:\")\n",
|
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"ELASTIC_CLOUD_PASSWORD = getpass.getpass(\"`elastic` user password:\")\n",
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||
"\n",
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||
"es = Elasticsearch(\n",
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||
" cloud_id=ELASTIC_CLOUD_ID,\n",
|
||
" basic_auth=(\"elastic\", ELASTIC_CLOUD_PASSWORD)\n",
|
||
")\n",
|
||
"print(es.info())"
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||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "mNrJvFU1Yagd",
|
||
"outputId": "db8829bf-bd59-4d86-8b73-c956d09f373e"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
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||
"name": "stdout",
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||
"text": [
|
||
"Cloud ID:··········\n",
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||
"`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",
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||
"\n",
|
||
"Let's start by downloading our test data."
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"source": [
|
||
"import requests\n",
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||
"\n",
|
||
"# URL of the raw file on GitHub\n",
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||
"file_url = \"https://github.com/elastic/eland/raw/main/docs/sphinx/examples/data/online-retail.csv.gz\"\n",
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||
"\n",
|
||
"# Local path where you want to save the file\n",
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||
"local_filename = \"online-retail.csv.gz\"\n",
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||
"\n",
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||
"# Send a GET request to the file URL\n",
|
||
"response = requests.get(file_url, stream=True)\n",
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||
"\n",
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||
"# 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",
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||
"\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",
|
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" 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 ... 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]"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>85123A</td>\n",
|
||
" <td>2.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>71053</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>2 rows × 8 columns</p>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>14998</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17419.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21773</td>\n",
|
||
" <td>1.25</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14999</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17419.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22149</td>\n",
|
||
" <td>2.10</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>2 rows × 8 columns</p>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>InvoiceDate</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>2010-12-01 08:26:00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>2010-12-01 08:26:00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>2010-12-01 08:26:00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>2010-12-01 08:26:00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>2010-12-01 08:26:00</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>5 rows × 2 columns</p>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1109</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12662.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22809</td>\n",
|
||
" <td>2.95</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1110</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12662.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84347</td>\n",
|
||
" <td>2.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1111</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12662.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84945</td>\n",
|
||
" <td>0.85</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1112</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12662.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22242</td>\n",
|
||
" <td>1.65</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1113</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12662.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22244</td>\n",
|
||
" <td>1.95</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>5 rows × 8 columns</p>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>85123A</td>\n",
|
||
" <td>2.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>71053</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84406B</td>\n",
|
||
" <td>2.75</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84029G</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84029E</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>5 rows × 8 columns</p>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>0 rows × 8 columns</p>"
|
||
]
|
||
},
|
||
"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 UnitPrice\n",
|
||
"sum 111960.000 61548.490000\n",
|
||
"mean 7.464 4.103233\n",
|
||
"max 2880.000 950.990000\n",
|
||
"min -9360.000 0.000000"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-cbfeceb1-2fd7-4a44-acdf-2b6c13dbb9c2\" class=\"colab-df-container\">\n",
|
||
" <div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Quantity</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>sum</th>\n",
|
||
" <td>111960.000</td>\n",
|
||
" <td>61548.490000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>7.464</td>\n",
|
||
" <td>4.103233</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>2880.000</td>\n",
|
||
" <td>950.990000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>-9360.000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbfeceb1-2fd7-4a44-acdf-2b6c13dbb9c2')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-cbfeceb1-2fd7-4a44-acdf-2b6c13dbb9c2 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-cbfeceb1-2fd7-4a44-acdf-2b6c13dbb9c2');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-a515a67f-153d-431f-a346-9ad3f849d5b9\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a515a67f-153d-431f-a346-9ad3f849d5b9')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-a515a67f-153d-431f-a346-9ad3f849d5b9 button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 21
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['Quantity','UnitPrice']].agg(['sum', 'mean', 'max', 'min'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "CklumXmiW_sm"
|
||
},
|
||
"source": [
|
||
"**Give me descriptive statistics for the entire data frame**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2021-12-15T20:25:22.712002Z",
|
||
"iopub.status.busy": "2021-12-15T20:25:22.711114Z",
|
||
"iopub.status.idle": "2021-12-15T20:25:22.982698Z",
|
||
"shell.execute_reply": "2021-12-15T20:25:22.981770Z"
|
||
},
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 297
|
||
},
|
||
"id": "AttDFOcRW_sm",
|
||
"outputId": "a515c9f5-5b04-4943-9c72-8da357fa29ae"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"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% 14225.913815 1.000000 1.336010\n",
|
||
"50% 15668.124797 2.423796 2.396465\n",
|
||
"75% 17195.974646 7.403795 4.282239\n",
|
||
"max 18239.000000 2880.000000 950.990000"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-cfeaff95-e82b-4d7c-bbde-299c17b9493e\" class=\"colab-df-container\">\n",
|
||
" <div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>Quantity</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>10729.000000</td>\n",
|
||
" <td>15000.000000</td>\n",
|
||
" <td>15000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>15590.776680</td>\n",
|
||
" <td>7.464000</td>\n",
|
||
" <td>4.103233</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>1764.189592</td>\n",
|
||
" <td>85.930116</td>\n",
|
||
" <td>20.106214</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>12347.000000</td>\n",
|
||
" <td>-9360.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>14225.913815</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.336010</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>15668.124797</td>\n",
|
||
" <td>2.423796</td>\n",
|
||
" <td>2.396465</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>17195.974646</td>\n",
|
||
" <td>7.403795</td>\n",
|
||
" <td>4.282239</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>18239.000000</td>\n",
|
||
" <td>2880.000000</td>\n",
|
||
" <td>950.990000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfeaff95-e82b-4d7c-bbde-299c17b9493e')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-cfeaff95-e82b-4d7c-bbde-299c17b9493e button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-cfeaff95-e82b-4d7c-bbde-299c17b9493e');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-51e65f9a-bb05-4074-a835-06c70a6b5749\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51e65f9a-bb05-4074-a835-06c70a6b5749')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-51e65f9a-bb05-4074-a835-06c70a6b5749 button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
" </div>\n",
|
||
" </div>\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": [
|
||
"<Figure size 1200x400 with 2 Axes>"
|
||
],
|
||
"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": [
|
||
"<Figure size 1200x400 with 2 Axes>"
|
||
],
|
||
"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
|
||
} |