mirror of
https://github.com/elastic/eland.git
synced 2025-07-11 00:02:14 +08:00
1636 lines
70 KiB
Plaintext
1636 lines
70 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2020-10-27T16:17:24.880429Z",
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||
"iopub.status.busy": "2020-10-27T16:17:24.880035Z",
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||
"iopub.status.idle": "2020-10-27T16:17:25.392040Z",
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"shell.execute_reply": "2020-10-27T16:17:25.391672Z"
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}
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},
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"outputs": [],
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"source": [
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"import eland as ed\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",
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"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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"metadata": {},
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"source": [
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"# Online Retail Analysis"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Getting Started\n",
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"\n",
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"To get started, let's create an `eland.DataFrame` by reading a csv file. This creates and populates the \n",
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"`online-retail` index in the local Elasticsearch cluster."
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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": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2020-10-27T16:17:25.397998Z",
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||
"iopub.status.busy": "2020-10-27T16:17:25.397644Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.819185Z",
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"shell.execute_reply": "2020-10-27T16:17:28.818652Z"
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}
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},
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"outputs": [],
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"source": [
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"df = ed.csv_to_eland(\"data/online-retail.csv.gz\",\n",
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" es_client='localhost', \n",
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" es_dest_index='online-retail', \n",
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" es_if_exists='replace', \n",
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" es_dropna=True,\n",
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" es_refresh=True,\n",
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" compression='gzip',\n",
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" index_col=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here we see that the `\"_id\"` field was used to index our data frame. "
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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": "2020-10-27T16:17:28.823297Z",
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||
"iopub.status.busy": "2020-10-27T16:17:28.822935Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.825526Z",
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||
"shell.execute_reply": "2020-10-27T16:17:28.825229Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'_id'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.index.es_index_field"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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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": 4,
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2020-10-27T16:17:28.828159Z",
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||
"iopub.status.busy": "2020-10-27T16:17:28.827813Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.830145Z",
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"shell.execute_reply": "2020-10-27T16:17:28.829779Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode',\n",
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" 'UnitPrice'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.columns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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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": 5,
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"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2020-10-27T16:17:28.833415Z",
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||
"iopub.status.busy": "2020-10-27T16:17:28.833059Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.835205Z",
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"shell.execute_reply": "2020-10-27T16:17:28.834894Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Country object\n",
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"CustomerID float64\n",
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"Description object\n",
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"InvoiceDate object\n",
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"InvoiceNo object\n",
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"Quantity int64\n",
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"StockCode object\n",
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"UnitPrice float64\n",
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"dtype: object"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.dtypes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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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": 6,
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"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2020-10-27T16:17:28.840515Z",
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||
"iopub.status.busy": "2020-10-27T16:17:28.840166Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.842305Z",
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||
"shell.execute_reply": "2020-10-27T16:17:28.841993Z"
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||
}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"es_index_pattern: online-retail\n",
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"Index:\n",
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" es_index_field: _id\n",
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" is_source_field: False\n",
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"Mappings:\n",
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" capabilities:\n",
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" es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name\n",
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"Country Country True keyword None object True True False Country\n",
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"CustomerID CustomerID True double None float64 True True False CustomerID\n",
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"Description Description True keyword None object True True False Description\n",
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"InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n",
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"InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n",
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"Quantity Quantity True long None int64 True True False Quantity\n",
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"StockCode StockCode True keyword None object True True False StockCode\n",
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"UnitPrice UnitPrice True double None float64 True True False UnitPrice\n",
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"Operations:\n",
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" tasks: []\n",
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" size: None\n",
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" sort_params: None\n",
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" _source: ['Country', 'CustomerID', 'Description', 'InvoiceDate', 'InvoiceNo', 'Quantity', 'StockCode', 'UnitPrice']\n",
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" body: {}\n",
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" post_processing: []\n",
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"\n"
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]
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}
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],
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"source": [
|
||
"print(df.es_info())"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"## Selecting and Indexing Data\n",
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||
"\n",
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||
"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."
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||
]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"### head and tail\n",
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||
"\n",
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||
"much like pandas, eland data frames offer `.head(n)` and `.tail(n)` methods that return the first and last n rows, respectively."
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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": 7,
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||
"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2020-10-27T16:17:28.845036Z",
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||
"iopub.status.busy": "2020-10-27T16:17:28.844630Z",
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||
"iopub.status.idle": "2020-10-27T16:17:28.868732Z",
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"shell.execute_reply": "2020-10-27T16:17:28.869091Z"
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}
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||
},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<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",
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||
" <th>...</th>\n",
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||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
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||
" <th>0</th>\n",
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||
" <td>United Kingdom</td>\n",
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||
" <td>17850.0</td>\n",
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||
" <td>...</td>\n",
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||
" <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>"
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||
],
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||
"text/plain": [
|
||
" Country CustomerID ... StockCode UnitPrice\n",
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||
"0 United Kingdom 17850.0 ... 85123A 2.55\n",
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||
"1 United Kingdom 17850.0 ... 71053 3.39\n",
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"\n",
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||
"[2 rows x 8 columns]"
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||
]
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||
},
|
||
"execution_count": 1,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"df.head(2)"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 8,
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||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2020-10-27T16:17:28.872591Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:28.872174Z",
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"iopub.status.idle": "2020-10-27T16:17:28.878217Z",
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"shell.execute_reply": "2020-10-27T16:17:28.877852Z"
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}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"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",
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"Country Country True keyword None object True True False Country\n",
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||
"CustomerID CustomerID True double None float64 True True False CustomerID\n",
|
||
"Description Description True keyword None object True True False Description\n",
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||
"InvoiceDate InvoiceDate True keyword None object True True False InvoiceDate\n",
|
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"InvoiceNo InvoiceNo True keyword None object True True False InvoiceNo\n",
|
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"Quantity Quantity True long None int64 True True False Quantity\n",
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"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())"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2020-10-27T16:17:28.881414Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:28.880806Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:28.905628Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:28.905926Z"
|
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}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
||
" .dataframe thead th {\n",
|
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" 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>12494</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>16710.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21587</td>\n",
|
||
" <td>2.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14448</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>14282.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>85099C</td>\n",
|
||
" <td>1.95</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>2 rows × 8 columns</p>"
|
||
],
|
||
"text/plain": [
|
||
" Country CustomerID ... StockCode UnitPrice\n",
|
||
"12494 United Kingdom 16710.0 ... 21587 2.55\n",
|
||
"14448 United Kingdom 14282.0 ... 85099C 1.95\n",
|
||
"\n",
|
||
"[2 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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": "2020-10-27T16:17:28.909532Z",
|
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"iopub.status.busy": "2020-10-27T16:17:28.909029Z",
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"shell.execute_reply": "2020-10-27T16:17:28.924423Z"
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}
|
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},
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"outputs": [
|
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{
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"data": {
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .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>"
|
||
],
|
||
"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": 1,
|
||
"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,
|
||
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|
||
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|
||
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|
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|
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|
||
"shell.execute_reply": "2020-10-27T16:17:28.957119Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'term': {'Country': 'Germany'}}\n"
|
||
]
|
||
},
|
||
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|
||
"data": {
|
||
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|
||
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|
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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>5067</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12738.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22952</td>\n",
|
||
" <td>0.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5070</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12738.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21977</td>\n",
|
||
" <td>0.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5071</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12738.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84991</td>\n",
|
||
" <td>0.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5072</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12738.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21212</td>\n",
|
||
" <td>0.55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5073</th>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>12738.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>POST</td>\n",
|
||
" <td>18.00</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>5 rows × 8 columns</p>"
|
||
],
|
||
"text/plain": [
|
||
" Country CustomerID ... StockCode UnitPrice\n",
|
||
"5067 Germany 12738.0 ... 22952 0.55\n",
|
||
"5070 Germany 12738.0 ... 21977 0.55\n",
|
||
"5071 Germany 12738.0 ... 84991 0.55\n",
|
||
"5072 Germany 12738.0 ... 21212 0.55\n",
|
||
"5073 Germany 12738.0 ... POST 18.00\n",
|
||
"\n",
|
||
"[5 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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": "2020-10-27T16:17:28.961855Z",
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|
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|
||
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|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'terms': {'Country': ['Germany', 'United States']}}\n"
|
||
]
|
||
},
|
||
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|
||
"data": {
|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>UnitPrice</th>\n",
|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
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|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>85123A</td>\n",
|
||
" <td>2.55</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>71053</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84406B</td>\n",
|
||
" <td>2.75</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84029G</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>United Kingdom</td>\n",
|
||
" <td>17850.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>84029E</td>\n",
|
||
" <td>3.39</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<p>5 rows × 8 columns</p>"
|
||
],
|
||
"text/plain": [
|
||
" Country CustomerID ... StockCode UnitPrice\n",
|
||
"0 United Kingdom 17850.0 ... 85123A 2.55\n",
|
||
"1 United Kingdom 17850.0 ... 71053 3.39\n",
|
||
"2 United Kingdom 17850.0 ... 84406B 2.75\n",
|
||
"3 United Kingdom 17850.0 ... 84029G 3.39\n",
|
||
"4 United Kingdom 17850.0 ... 84029E 3.39\n",
|
||
"\n",
|
||
"[5 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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,
|
||
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|
||
"execution": {
|
||
"iopub.execute_input": "2020-10-27T16:17:28.999512Z",
|
||
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|
||
"iopub.status.idle": "2020-10-27T16:17:29.036479Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.036094Z"
|
||
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|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
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|
||
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|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
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|
||
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|
||
" .dataframe thead th {\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>StockCode</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" </tbody>\n",
|
||
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|
||
"</div>\n",
|
||
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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": 1,
|
||
"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": "2020-10-27T16:17:29.043222Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.042872Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.044991Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.044580Z"
|
||
}
|
||
},
|
||
"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": "2020-10-27T16:17:29.048752Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.048204Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.053029Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.053330Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"16"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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": "2020-10-27T16:17:29.056956Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.056547Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.061156Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.060738Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"111960"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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": "2020-10-27T16:17:29.065013Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.064600Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.073924Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.074225Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Quantity</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>sum</th>\n",
|
||
" <td>111960.000</td>\n",
|
||
" <td>61548.490000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>7.464</td>\n",
|
||
" <td>4.103233</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>2880.000</td>\n",
|
||
" <td>950.990000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>-9360.000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Quantity UnitPrice\n",
|
||
"sum 111960.000 61548.490000\n",
|
||
"mean 7.464 4.103233\n",
|
||
"max 2880.000 950.990000\n",
|
||
"min -9360.000 0.000000"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['Quantity','UnitPrice']].agg(['sum', 'mean', 'max', 'min'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Give me descriptive statistics for the entire data frame**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2020-10-27T16:17:29.077672Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.077310Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.153081Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.153392Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>CustomerID</th>\n",
|
||
" <th>Quantity</th>\n",
|
||
" <th>UnitPrice</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>10729.000000</td>\n",
|
||
" <td>15000.000000</td>\n",
|
||
" <td>15000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>15590.776680</td>\n",
|
||
" <td>7.464000</td>\n",
|
||
" <td>4.103233</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>1764.025160</td>\n",
|
||
" <td>85.924387</td>\n",
|
||
" <td>20.104873</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>14223.005405</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.250000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>15664.392622</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>2.510000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>17218.865385</td>\n",
|
||
" <td>6.422414</td>\n",
|
||
" <td>4.213396</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>18239.000000</td>\n",
|
||
" <td>2880.000000</td>\n",
|
||
" <td>950.990000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" CustomerID Quantity UnitPrice\n",
|
||
"count 10729.000000 15000.000000 15000.000000\n",
|
||
"mean 15590.776680 7.464000 4.103233\n",
|
||
"std 1764.025160 85.924387 20.104873\n",
|
||
"min 12347.000000 -9360.000000 0.000000\n",
|
||
"25% 14223.005405 1.000000 1.250000\n",
|
||
"50% 15664.392622 2.000000 2.510000\n",
|
||
"75% 17218.865385 6.422414 4.213396\n",
|
||
"max 18239.000000 2880.000000 950.990000"
|
||
]
|
||
},
|
||
"execution_count": 1,
|
||
"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": "2020-10-27T16:17:29.161424Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.160640Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:29.420989Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:29.420512Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[(df['Quantity']>-50) & \n",
|
||
" (df['Quantity']<50) & \n",
|
||
" (df['UnitPrice']>0) & \n",
|
||
" (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2020-10-27T16:17:29.428090Z",
|
||
"iopub.status.busy": "2020-10-27T16:17:29.427699Z",
|
||
"iopub.status.idle": "2020-10-27T16:17:30.159656Z",
|
||
"shell.execute_reply": "2020-10-27T16:17:30.159959Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[(df['Quantity']>-50) & \n",
|
||
" (df['Quantity']<50) & \n",
|
||
" (df['UnitPrice']>0) & \n",
|
||
" (df['UnitPrice']<100)][['Quantity', 'UnitPrice']].hist(figsize=[12,4], bins=30, log=True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
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