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new 0dfd99e381 202307-notebook-unionall (#14726)
0dfd99e381 is described below
commit 0dfd99e3811101ad0b522a7da9c9ed30bd60f558
Author: Peter Marshall <[email protected]>
AuthorDate: Mon Aug 21 18:55:58 2023 +0100
202307-notebook-unionall (#14726)
Co-authored-by: Victoria Lim <[email protected]>
Co-authored-by: Charles Smith <[email protected]>
---
.../notebooks/03-query/04-UnionOperations.ipynb | 509 +++++++++++++++++++++
1 file changed, 509 insertions(+)
diff --git
a/examples/quickstart/jupyter-notebooks/notebooks/03-query/04-UnionOperations.ipynb
b/examples/quickstart/jupyter-notebooks/notebooks/03-query/04-UnionOperations.ipynb
new file mode 100644
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b/examples/quickstart/jupyter-notebooks/notebooks/03-query/04-UnionOperations.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "557e06e8-9b35-4b34-8322-8a8ede6de709",
+ "metadata": {},
+ "source": [
+ "# Using `UNION ALL` to address multiple `TABLE`s in the same query\n",
+ "\n",
+ "<!--\n",
+ " ~ Licensed to the Apache Software Foundation (ASF) under one\n",
+ " ~ or more contributor license agreements. See the NOTICE file\n",
+ " ~ distributed with this work for additional information\n",
+ " ~ regarding copyright ownership. The ASF licenses this file\n",
+ " ~ to you under the Apache License, Version 2.0 (the\n",
+ " ~ \"License\"); you may not use this file except in compliance\n",
+ " ~ with the License. You may obtain a copy of the License at\n",
+ " ~\n",
+ " ~ http://www.apache.org/licenses/LICENSE-2.0\n";,
+ " ~\n",
+ " ~ Unless required by applicable law or agreed to in writing,\n",
+ " ~ software distributed under the License is distributed on an\n",
+ " ~ \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ " ~ KIND, either express or implied. See the License for the\n",
+ " ~ specific language governing permissions and limitations\n",
+ " ~ under the License.\n",
+ " -->\n",
+ " \n",
+ "While working with Druid, you may need to bring together two different
tables of results together into a single result list, or to treat multiple
tables as a single input to a query. This notebook introduces the `UNION ALL`
operator, walking through two ways in which this operator can be used to
achieve this result: top-level and table-level `UNION ALL`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf4554ae-6516-4e76-b202-d6e2fdf31603",
+ "metadata": {},
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "This tutorial works with Druid 26.0.0 or later.\n",
+ "\n",
+ "#### Run using Docker\n",
+ "\n",
+ "Launch this tutorial and all prerequisites using the `druid-jupyter`
profile of the Docker Compose file for Jupyter-based Druid tutorials. For more
information, see [Docker for Jupyter Notebook
tutorials](https://druid.apache.org/docs/latest/tutorials/tutorial-jupyter-docker.html).\n",
+ " \n",
+ "#### Run Druid without Docker\n",
+ "\n",
+ "If you do not use the Docker Compose environment, you need the
following:\n",
+ "\n",
+ "* A running Druid instance, with a `DRUID_HOST` local environment
variable containing the servername of your Druid router\n",
+ "*
[druidapi](https://github.com/apache/druid/blob/master/examples/quickstart/jupyter-notebooks/druidapi/README.md),
a Python client for Apache Druid. Follow the instructions in the Install
section of the README file."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee0c3171-def8-4ad9-9c56-d3a67f309631",
+ "metadata": {},
+ "source": [
+ "### Initialization\n",
+ "\n",
+ "Run the next cell to attempt a connection to Druid services. If
successful, the output shows the Druid version number."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9fa4abfe-f878-4031-88f2-94c13e922279",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import druidapi\n",
+ "import os\n",
+ "\n",
+ "if 'DRUID_HOST' not in os.environ.keys():\n",
+ " druid_host=f\"http://localhost:8888\"\n";,
+ "else:\n",
+ " druid_host=f\"http://{os.environ['DRUID_HOST']}:8888\"\n",
+ " \n",
+ "print(f\"Opening a connection to {druid_host}.\")\n",
+ "druid = druidapi.jupyter_client(druid_host)\n",
+ "\n",
+ "display = druid.display\n",
+ "sql_client = druid.sql\n",
+ "status_client = druid.status\n",
+ "\n",
+ "status_client.version"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc3001a0-27e5-4f41-876a-ce6eab2acd6a",
+ "metadata": {},
+ "source": [
+ "Finally, run the following cell to import the Python JSON module."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b058d8b-2bae-4929-ab0c-5a6df1850387",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f388633f-195b-4381-98cc-7a2f80f48690",
+ "metadata": {},
+ "source": [
+ "## Using Top-level `UNION ALL` to concatenate result sets\n",
+ "\n",
+ "Run the following cell to ingest the wikipedia data example. Once
completed, you will see a description of the new table.\n",
+ "\n",
+ "You can optionally monitor the ingestion in the Druid console while it
runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a399196b-12db-42ff-ae24-c7232f150aba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql='''\n",
+ "REPLACE INTO \"example-wikipedia-unionall\" OVERWRITE ALL\n",
+ "WITH \"ext\" AS (SELECT *\n",
+ "FROM TABLE(\n",
+ " EXTERN(\n",
+ "
'{\"type\":\"http\",\"uris\":[\"https://druid.apache.org/data/wikipedia.json.gz\"]}',\n",
+ " '{\"type\":\"json\"}'\n",
+ " )\n",
+ ") EXTEND (\"isRobot\" VARCHAR, \"channel\" VARCHAR, \"timestamp\"
VARCHAR, \"flags\" VARCHAR, \"isUnpatrolled\" VARCHAR, \"page\" VARCHAR,
\"diffUrl\" VARCHAR, \"added\" BIGINT, \"comment\" VARCHAR, \"commentLength\"
BIGINT, \"isNew\" VARCHAR, \"isMinor\" VARCHAR, \"delta\" BIGINT,
\"isAnonymous\" VARCHAR, \"user\" VARCHAR, \"deltaBucket\" BIGINT, \"deleted\"
BIGINT, \"namespace\" VARCHAR, \"cityName\" VARCHAR, \"countryName\" VARCHAR,
\"regionIsoCode\" VARCHAR, \"metroCode\" BIGINT [...]
+ "SELECT\n",
+ " TIME_PARSE(\"timestamp\") AS \"__time\",\n",
+ " \"isRobot\",\n",
+ " \"channel\",\n",
+ " \"page\",\n",
+ " \"commentLength\",\n",
+ " \"countryName\",\n",
+ " \"user\"\n",
+ "FROM \"ext\"\n",
+ "PARTITIONED BY DAY\n",
+ "'''\n",
+ "\n",
+ "sql_client.run_task(sql)\n",
+ "sql_client.wait_until_ready('example-wikipedia-unionall')\n",
+ "display.table('example-wikipedia-unionall')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24b47cc3-68f5-4a73-b374-94bbfa32d91d",
+ "metadata": {},
+ "source": [
+ "You can use `UNION ALL` to append the results of one query with
another.\n",
+ "\n",
+ "The first query in the cell below, `set1`, returns the ten first edits to
any \"fr\"-like `channel` between midday and 1pm on the 27th June 2016. The
second query repeats this but for any \"en\"-like `channel`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b76e5184-9fe4-4f21-a471-4e15d16515c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql = '''\n",
+ "WITH\n",
+ "set1 AS (\n",
+ " SELECT\n",
+ " __time,\n",
+ " \"channel\",\n",
+ " \"page\",\n",
+ " \"isRobot\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE DATE_TRUNC('HOUR', __time) = TIMESTAMP '2016-06-27 12:00:00'\n",
+ " AND channel LIKE '#fr%'\n",
+ " ORDER BY __time\n",
+ " LIMIT 10\n",
+ " ),\n",
+ "set2 AS (\n",
+ " SELECT\n",
+ " __time,\n",
+ " \"channel\",\n",
+ " \"page\",\n",
+ " \"isRobot\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE DATE_TRUNC('HOUR', __time) = TIMESTAMP '2016-06-27 12:00:00'\n",
+ " AND channel LIKE '#en%'\n",
+ " ORDER BY __time\n",
+ " LIMIT 10\n",
+ " )\n",
+ " \n",
+ "SELECT * from set1\n",
+ "UNION ALL\n",
+ "SELECT * from set2\n",
+ "'''\n",
+ "\n",
+ "display.sql(sql)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5e77fa9-a60c-4955-b763-58d970d7326d",
+ "metadata": {},
+ "source": [
+ "This is a
[top-level](https://druid.apache.org/docs/latest/querying/sql.html#top-level)
`UNION` operation. First, Druid calculated `set1` and appended subsequent
results sets.\n",
+ "\n",
+ "Notice that these results are not in order by time – even though the
individual sets did `ORDER BY` time. Druid simply concatenated the two result
sets together.\n",
+ "\n",
+ "Optionally, run the next cell to show the precise [`EXPLAIN
PLAN`](https://druid.apache.org/docs/latest/querying/sql-translation#interpreting-explain-plan-output)
for the query. You can see there are two `query` execution plans, one for each
subquery. Also, Druid's planning process optimized execution of the outer
query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97934da2-17d1-4c91-8ae3-926cc89185c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(json.dumps(json.loads(sql_client.explain_sql(sql)['PLAN']),
indent=2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "800add1a-d459-4796-b974-b2f094db417f",
+ "metadata": {},
+ "source": [
+ "Run next cell to perform another top-level UNION ALL, this time where the
sets use `GROUP BY`.\n",
+ "\n",
+ "Notice that the aggregates have `AS` to set specific field names."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e687466-74bb-4cc0-ba17-913d1807fc60",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql='''\n",
+ "WITH\n",
+ "set1 AS (\n",
+ " SELECT\n",
+ " TIME_FLOOR(__time, 'PT1H') AS \"Period\",\n",
+ " countryName,\n",
+ " AVG(commentLength) AS \"Average Comment Size\",\n",
+ " COUNT(DISTINCT \"page\") AS \"Pages\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE countryName='China'\n",
+ " GROUP BY 1, 2\n",
+ " LIMIT 10\n",
+ " ),\n",
+ "set2 AS (\n",
+ " SELECT\n",
+ " TIME_FLOOR(__time, 'PT1H') AS \"Episode\",\n",
+ " countryName,\n",
+ " COUNT(DISTINCT \"page\") AS \"Pages\",\n",
+ " AVG(commentLength) AS \"Average Comment Length\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE countryName='Austria'\n",
+ " GROUP BY 1, 2\n",
+ " LIMIT 10\n",
+ " )\n",
+ "\n",
+ "SELECT * from set1\n",
+ "UNION ALL\n",
+ "SELECT * from set2\n",
+ "'''\n",
+ "\n",
+ "display.sql(sql)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2c95ffc-b260-4671-bacc-c8cc3137e9c2",
+ "metadata": {},
+ "source": [
+ "Look carefully at these results - Druid has simply appended the results
from `set2` to `set1` without introducing redundant columns.\n",
+ "\n",
+ "* Column name in `set2` (`Period` versus `Episode` and `Average Comment
Size` versus `Average Comment Length`) did not result in new columns\n",
+ "* Columns with the same name (`Pages`) did not result in that aggregate
being put into same column - Austria's values are simply appended `Average
Comment Size`\n",
+ "\n",
+ "Run the next cell, which uses explicit column names at the top-level,
rather than `*`, to ensure the calculations appear in the right columns in the
final result. It also aliases the columns for the results by using `AS`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39f9be92-7b2e-417c-b16a-5060b8cd2c30",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql='''\n",
+ "WITH\n",
+ "set1 AS (\n",
+ " SELECT\n",
+ " TIME_FLOOR(__time, 'PT1H') AS \"Period\",\n",
+ " countryName,\n",
+ " AVG(commentLength) AS \"Average Comment Size\",\n",
+ " COUNT(DISTINCT \"page\") AS \"Pages\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE countryName='China'\n",
+ " GROUP BY 1, 2\n",
+ " LIMIT 10\n",
+ " ),\n",
+ "set2 AS (\n",
+ " SELECT\n",
+ " TIME_FLOOR(__time, 'PT1H') AS \"Episode\",\n",
+ " countryName,\n",
+ " COUNT(DISTINCT \"page\") AS \"Pages\",\n",
+ " AVG(commentLength) AS \"Average Comment Length\"\n",
+ " FROM \"example-wikipedia-unionall\"\n",
+ " WHERE countryName='Austria'\n",
+ " GROUP BY 1, 2\n",
+ " LIMIT 10\n",
+ " )\n",
+ "\n",
+ "SELECT \"Period\", \"countryName\" AS \"Country\", \"Average Comment
Size\" AS \"Edit Size\", \"Pages\" AS \"Unique Pages\" from set1\n",
+ "UNION ALL\n",
+ "SELECT \"Episode\", \"countryName\", \"Average Comment Length\",
\"Pages\" from set2\n",
+ "'''\n",
+ "\n",
+ "display.sql(sql)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25001794-e1a7-4325-adb3-2b8f26036261",
+ "metadata": {},
+ "source": [
+ "## Using Table-level `UNION ALL` to work with multiple tables\n",
+ "\n",
+ "From one source of data, data engineers may create multiple `TABLE`
datasources in order to:\n",
+ "\n",
+ "* Separate data with different levels of `__time` granularity (ie. the
level of summarisation),\n",
+ "* Apply different security to different parts, for example, per
tenant,\n",
+ "* Break up the data using filtering at ingestion time, for example,
different tables for different HTTP error codes,\n",
+ "* Separate upstream data by the source device or system, for example,
different types of IOT device,\n",
+ "* Isolate different periods of time, perhaps with different retention
periods.\n",
+ "\n",
+ "You can use `UNION ALL` to access _all_ the source data, referencing all
the `TABLE` datasources through a sub-query or a `FROM` clause.\n",
+ "\n",
+ "The next two cells create two new tables, `example-wikipedia-unionall-en`
and `example-wikipedia-unionall-fr`. `example-wikipedia-unionall-en` contains
only data for English language channel edits, while
`example-wikipedia-unionall-fr` contains only French channels.\n",
+ "\n",
+ "Run the next two cells, monitoring the ingestion in the Druid Console as
they run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "269c6aef-c3a5-46ad-8332-30b7bf30ddfb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql='''\n",
+ "REPLACE INTO \"example-wikipedia-unionall-en\" OVERWRITE ALL\n",
+ "WITH \"ext\" AS (SELECT *\n",
+ "FROM TABLE(\n",
+ " EXTERN(\n",
+ "
'{\"type\":\"http\",\"uris\":[\"https://druid.apache.org/data/wikipedia.json.gz\"]}',\n",
+ " '{\"type\":\"json\"}'\n",
+ " )\n",
+ ") EXTEND (\"isRobot\" VARCHAR, \"channel\" VARCHAR, \"timestamp\"
VARCHAR, \"flags\" VARCHAR, \"isUnpatrolled\" VARCHAR, \"page\" VARCHAR,
\"diffUrl\" VARCHAR, \"added\" BIGINT, \"comment\" VARCHAR, \"commentLength\"
BIGINT, \"isNew\" VARCHAR, \"isMinor\" VARCHAR, \"delta\" BIGINT,
\"isAnonymous\" VARCHAR, \"user\" VARCHAR, \"deltaBucket\" BIGINT, \"deleted\"
BIGINT, \"namespace\" VARCHAR, \"cityName\" VARCHAR, \"countryName\" VARCHAR,
\"regionIsoCode\" VARCHAR, \"metroCode\" BIGINT [...]
+ "SELECT\n",
+ " TIME_PARSE(\"timestamp\") AS \"__time\",\n",
+ " \"isRobot\",\n",
+ " \"channel\",\n",
+ " \"page\",\n",
+ " \"commentLength\",\n",
+ " \"countryName\",\n",
+ " \"user\"\n",
+ "FROM \"ext\"\n",
+ "WHERE \"channel\" LIKE '#en%'\n",
+ "PARTITIONED BY DAY\n",
+ "'''\n",
+ "\n",
+ "sql_client.run_task(sql)\n",
+ "sql_client.wait_until_ready('example-wikipedia-unionall-en')\n",
+ "display.table('example-wikipedia-unionall-en')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61740d61-28fc-48e9-b026-d472bd04f390",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql='''\n",
+ "REPLACE INTO \"example-wikipedia-unionall-fr\" OVERWRITE ALL\n",
+ "WITH \"ext\" AS (SELECT *\n",
+ "FROM TABLE(\n",
+ " EXTERN(\n",
+ "
'{\"type\":\"http\",\"uris\":[\"https://druid.apache.org/data/wikipedia.json.gz\"]}',\n",
+ " '{\"type\":\"json\"}'\n",
+ " )\n",
+ ") EXTEND (\"isRobot\" VARCHAR, \"channel\" VARCHAR, \"timestamp\"
VARCHAR, \"flags\" VARCHAR, \"isUnpatrolled\" VARCHAR, \"page\" VARCHAR,
\"diffUrl\" VARCHAR, \"added\" BIGINT, \"comment\" VARCHAR, \"commentLength\"
BIGINT, \"isNew\" VARCHAR, \"isMinor\" VARCHAR, \"delta\" BIGINT,
\"isAnonymous\" VARCHAR, \"user\" VARCHAR, \"deltaBucket\" BIGINT, \"deleted\"
BIGINT, \"namespace\" VARCHAR, \"cityName\" VARCHAR, \"countryName\" VARCHAR,
\"regionIsoCode\" VARCHAR, \"metroCode\" BIGINT [...]
+ "SELECT\n",
+ " TIME_PARSE(\"timestamp\") AS \"__time\",\n",
+ " \"isRobot\",\n",
+ " \"channel\",\n",
+ " \"page\",\n",
+ " \"commentLength\",\n",
+ " \"countryName\",\n",
+ " \"user\"\n",
+ "FROM \"ext\"\n",
+ "WHERE \"channel\" LIKE '#fr%'\n",
+ "PARTITIONED BY DAY\n",
+ "'''\n",
+ "\n",
+ "sql_client.run_task(sql)\n",
+ "sql_client.wait_until_ready('example-wikipedia-unionall-fr')\n",
+ "display.table('example-wikipedia-unionall-fr')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8bbf2c6-681a-46f5-82f2-201cbbe8058d",
+ "metadata": {},
+ "source": [
+ "The next cell uses `UNION ALL` in a `WITH` statement that creates
`unifiedSource`. This will be a unified source of data for both tables that can
then be used in a `SELECT` query.\n",
+ "\n",
+ "Druid executes these \"[top
level](https://druid.apache.org/docs/26.0.0/querying/sql.html#top-level)\"
`UNION ALL` queries differently to \"[table
level](https://druid.apache.org/docs/26.0.0/querying/sql.html#table-level)\"
queries you have used so far. Table level `UNION ALL` makes use of `union`
datasources, and it's important that you read the
[documentation](https://druid.apache.org/docs/26.0.0/querying/datasource.html#union)
to understand the functionality available to you. Ope [...]
+ "\n",
+ "Run the following cell to count the number of robot and non-robot edits
by channel across both sets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "783fe77d-2e7b-476a-9748-67ea90c8bb91",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql = '''\n",
+ "WITH unifiedSource AS (\n",
+ " SELECT\n",
+ " \"__time\",\n",
+ " \"isRobot\",\n",
+ " \"channel\",\n",
+ " \"user\",\n",
+ " \"countryName\"\n",
+ " FROM \"example-wikipedia-unionall-en\"\n",
+ " UNION ALL\n",
+ " SELECT\n",
+ " \"__time\",\n",
+ " \"isRobot\",\n",
+ " \"channel\",\n",
+ " \"user\",\n",
+ " \"countryName\"\n",
+ " FROM \"example-wikipedia-unionall-fr\"\n",
+ " )\n",
+ "\n",
+ "SELECT\n",
+ " \"channel\",\n",
+ " COUNT(*) FILTER (WHERE isRobot=true) AS \"Robot Edits\",\n",
+ " COUNT (DISTINCT user) FILTER (WHERE isRobot=true) AS \"Robot
Editors\",\n",
+ " COUNT(*) FILTER (WHERE isRobot=false) AS \"Human Edits\",\n",
+ " COUNT (DISTINCT user) FILTER (WHERE isRobot=false) AS \"Human
Editors\"\n",
+ "FROM unifiedSource\n",
+ "GROUP BY 1\n",
+ "'''\n",
+ "\n",
+ "display.sql(sql)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f58a1846-5072-4495-b840-a620de3c0442",
+ "metadata": {},
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "* There are two modes for `UNION ALL` in Druid - top level and table
level\n",
+ "* Top level is a simple concatenation, and operations must be done on the
source `TABLE`s\n",
+ "* Table level uses a `union` data source, and operations must be done on
the outer `SELECT`\n",
+ "\n",
+ "## Learn more\n",
+ "\n",
+ "* Watch [Plan your Druid table
datasources](https://youtu.be/OpYDX4RYLV0?list=PLDZysOZKycN7MZvNxQk_6RbwSJqjSrsNR)
by Peter Marshall\n",
+ "* Read about
[union](https://druid.apache.org/docs/26.0.0/querying/datasource.html#union)
datasources in the documentation\n",
+ "* Read the latest
[documentation](https://druid.apache.org/docs/26.0.0/querying/sql.html#union-all)
on the `UNION ALL` operator"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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