Removed the jupyter connector.
This commit is contained in:
parent
e949d7467f
commit
3a2b9b1b2a
GPG Key ID:
DD53EAC36AFB61B4
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@ -1,288 +0,0 @@
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{
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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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"id": "f536eab6-2776-4535-972a-3ece6be99b3a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"\n",
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"warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6597a28-a4fc-46bb-a9b2-3cafbc944ef5",
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"metadata": {},
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"source": [
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"# Start here\n",
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"### Install couchbase and pandas with specified versions\n",
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"The cell below installs the couchbase python package and pandas."
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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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"id": "0c9dbddc-f5f5-4f58-a2e2-dfff0faf517f",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install couchbase==4.1.1 -q\n",
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"!pip install pandas==1.5.2 -q"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f41454c-0bf0-4e53-992b-34fb47bf7dd1",
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"metadata": {},
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"source": [
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"Import the the classes necessary to run the couchbase connector."
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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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"id": "5c4c7d02-c06b-49c3-a6e6-9819e9eae44b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from couchbase.cluster import Cluster\n",
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"from couchbase.auth import PasswordAuthenticator\n",
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"\n",
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"from couchbase.options import ClusterOptions, QueryOptions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e5173f8-7c64-40f4-8fbe-e3bf28ad96a3",
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"metadata": {},
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"source": [
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"Connect to the cluster select the sample bucket and connect to the default collection."
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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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"id": "66ada60d-8a49-473b-bc38-a40999ba761a",
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"metadata": {},
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"outputs": [],
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"source": [
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"cluster = Cluster.connect(\n",
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" \"couchbase://couchbase1\",\n",
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" ClusterOptions(\n",
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" PasswordAuthenticator(\"Administrator\", \"some-pw-that-is-better-than-this!\")\n",
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" ),\n",
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")\n",
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"bucket = cluster.bucket(\"travel-sample\")\n",
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"\n",
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"# get a reference to the default collection\n",
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"cb_coll = bucket.default_collection()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3dbc2840-9793-4a92-802b-78d0b333e632",
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"metadata": {},
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"source": [
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"Acces a document by key."
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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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"id": "5e5fd8c8-200f-4857-b63d-1172b842f11c",
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"metadata": {},
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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': 10,\n",
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" 'type': 'airline',\n",
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" 'name': '40-Mile Air',\n",
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" 'iata': 'Q5',\n",
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" 'icao': 'MLA',\n",
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" 'callsign': 'MILE-AIR',\n",
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" 'country': 'United States'}"
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]
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},
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"execution_count": 5,
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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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"# get a document by key\n",
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"result = cb_coll.get(\"airline_10\")\n",
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"result.value"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e818c095-e2d9-40c7-9234-ceddcb27cddb",
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"metadata": {},
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"source": [
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"The Querry language contains a \n",
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"If a hyphen is used please use a pair of \\` to surround the name using a hyphen."
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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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"id": "8eb6d0be-9fc0-4c89-ad18-801c0349d5cc",
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"metadata": {},
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 1968 entries, 0 to 1967\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 airportname 1968 non-null object\n",
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" 1 city 1968 non-null object\n",
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" 2 country 1968 non-null object\n",
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" 3 faa 1709 non-null object\n",
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" 4 geo 1968 non-null object\n",
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" 5 icao 1687 non-null object\n",
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" 6 id 1968 non-null int64 \n",
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" 7 type 1968 non-null object\n",
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" 8 tz 1968 non-null object\n",
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"dtypes: int64(1), object(8)\n",
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"memory usage: 138.5+ KB\n"
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]
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}
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],
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"source": [
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"result = cluster.query(\n",
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" \"\"\"\n",
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" SELECT airport.* \n",
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" FROM `travel-sample`.inventory.airport airport\n",
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" \"\"\",\n",
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" QueryOptions(metrics=True),\n",
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")\n",
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"df = pd.DataFrame(result)\n",
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"df.info()"
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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": 11,
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"id": "c1237c23-2ab4-4fa0-9895-0691c20827b6",
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"metadata": {},
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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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"\\begin{tabular}{llll}\n",
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"\\toprule\n",
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"{} & airportname & city & country \\\\\n",
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"\\midrule\n",
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"\\textbf{4877} & London Heliport & London & United Kingdom \\\\\n",
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"\\textbf{4289} & London St Pancras & London & United Kingdom \\\\\n",
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"\\textbf{3411} & Stansted & London & United Kingdom \\\\\n",
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"\\textbf{6457} & London - Kings Cross & London & United Kingdom \\\\\n",
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"\\textbf{8759} & Charles De Gaulle & Paris & France \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n"
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]
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}
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],
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"source": [
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"result = cluster.query(\n",
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" \"\"\"\n",
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" SELECT airport.airportname, airport.city, airport.country \n",
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" FROM `travel-sample`.inventory.airport airport INNER JOIN `travel-sample`.inventory.landmark landmark on airport.city = landmark.city\n",
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" \"\"\",\n",
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" QueryOptions(metrics=True),\n",
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")\n",
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"print(pd.DataFrame(result).sample(5).to_latex(bold_rows=True))"
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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": 13,
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"id": "a4cc7ef5-511e-4709-9d82-e1c20238f173",
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"metadata": {},
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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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"\\begin{tabular}{lllll}\n",
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"\\toprule\n",
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"{} & airportname & city & country & name \\\\\n",
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"\\midrule\n",
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"\\textbf{443 } & Edinburgh & Edinburgh & United Kingdom & Murrayfield Ice Rink \\\\\n",
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"\\textbf{1487} & Luton & London & United Kingdom & The Phoenix \\\\\n",
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"\\textbf{3620} & Paddington Station & London & United Kingdom & Kensal Green Cemetery \\\\\n",
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"\\textbf{5629} & London-Corbin Airport-MaGee Field & London & United States & Alexandra Palace \\\\\n",
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"\\textbf{8588} & Le Bourget & Paris & France & Aux Merveilleux de Fred \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n"
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]
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}
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],
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"source": [
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"result = cluster.query(\n",
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" \"\"\"\n",
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" SELECT airport.airportname, airport.city, airport.country, landmark.name\n",
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" FROM `travel-sample`.inventory.airport airport INNER JOIN `travel-sample`.inventory.landmark landmark on airport.city = landmark.city\n",
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" \"\"\",\n",
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" QueryOptions(metrics=True),\n",
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")\n",
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"print(pd.DataFrame(result).sample(5).sort_index().to_latex(bold_rows=True))"
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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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"# End here\n",
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"\n",
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"This should not be shownm"
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],
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "invalid syntax (2007221166.py, line 3)",
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"output_type": "error",
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"traceback": [
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"\u001B[1;36m File \u001B[1;32m\"C:\\Users\\phhor\\AppData\\Local\\Temp\\ipykernel_2708\\2007221166.py\"\u001B[1;36m, line \u001B[1;32m3\u001B[0m\n\u001B[1;33m This should not be shownm\u001B[0m\n\u001B[1;37m ^\u001B[0m\n\u001B[1;31mSyntaxError\u001B[0m\u001B[1;31m:\u001B[0m invalid syntax\n"
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]
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}
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],
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"execution_count": 1
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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