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langchain/docs/versioned_docs/version-0.2.x/integrations/document_loaders/pyspark_dataframe.ipynb
Jacob Lee aff771923a Jacob/new docs (#20570)
Use docusaurus versioning with a callout, merged master as well

@hwchase17 @baskaryan

---------

Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
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2024-04-18 11:10:55 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PySpark\n",
"\n",
"This notebook goes over how to load data from a [PySpark](https://spark.apache.org/docs/latest/api/python/) DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet pyspark"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from pyspark.sql import SparkSession"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting default log level to \"WARN\".\n",
"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
"23/05/31 14:08:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
]
}
],
"source": [
"spark = SparkSession.builder.getOrCreate()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = spark.read.csv(\"example_data/mlb_teams_2012.csv\", header=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import PySparkDataFrameLoader"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"loader = PySparkDataFrameLoader(spark, df, page_content_column=\"Team\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Stage 8:> (0 + 1) / 1]\r"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': ' 81.34', ' \"Wins\"': ' 98'}),\n",
" Document(page_content='Reds', metadata={' \"Payroll (millions)\"': ' 82.20', ' \"Wins\"': ' 97'}),\n",
" Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': ' 197.96', ' \"Wins\"': ' 95'}),\n",
" Document(page_content='Giants', metadata={' \"Payroll (millions)\"': ' 117.62', ' \"Wins\"': ' 94'}),\n",
" Document(page_content='Braves', metadata={' \"Payroll (millions)\"': ' 83.31', ' \"Wins\"': ' 94'}),\n",
" Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': ' 55.37', ' \"Wins\"': ' 94'}),\n",
" Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': ' 120.51', ' \"Wins\"': ' 93'}),\n",
" Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': ' 81.43', ' \"Wins\"': ' 93'}),\n",
" Document(page_content='Rays', metadata={' \"Payroll (millions)\"': ' 64.17', ' \"Wins\"': ' 90'}),\n",
" Document(page_content='Angels', metadata={' \"Payroll (millions)\"': ' 154.49', ' \"Wins\"': ' 89'}),\n",
" Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': ' 132.30', ' \"Wins\"': ' 88'}),\n",
" Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': ' 110.30', ' \"Wins\"': ' 88'}),\n",
" Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': ' 95.14', ' \"Wins\"': ' 86'}),\n",
" Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': ' 96.92', ' \"Wins\"': ' 85'}),\n",
" Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': ' 97.65', ' \"Wins\"': ' 83'}),\n",
" Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': ' 174.54', ' \"Wins\"': ' 81'}),\n",
" Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': ' 74.28', ' \"Wins\"': ' 81'}),\n",
" Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': ' 63.43', ' \"Wins\"': ' 79'}),\n",
" Document(page_content='Padres', metadata={' \"Payroll (millions)\"': ' 55.24', ' \"Wins\"': ' 76'}),\n",
" Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': ' 81.97', ' \"Wins\"': ' 75'}),\n",
" Document(page_content='Mets', metadata={' \"Payroll (millions)\"': ' 93.35', ' \"Wins\"': ' 74'}),\n",
" Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': ' 75.48', ' \"Wins\"': ' 73'}),\n",
" Document(page_content='Royals', metadata={' \"Payroll (millions)\"': ' 60.91', ' \"Wins\"': ' 72'}),\n",
" Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': ' 118.07', ' \"Wins\"': ' 69'}),\n",
" Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': ' 173.18', ' \"Wins\"': ' 69'}),\n",
" Document(page_content='Indians', metadata={' \"Payroll (millions)\"': ' 78.43', ' \"Wins\"': ' 68'}),\n",
" Document(page_content='Twins', metadata={' \"Payroll (millions)\"': ' 94.08', ' \"Wins\"': ' 66'}),\n",
" Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': ' 78.06', ' \"Wins\"': ' 64'}),\n",
" Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': ' 88.19', ' \"Wins\"': ' 61'}),\n",
" Document(page_content='Astros', metadata={' \"Payroll (millions)\"': ' 60.65', ' \"Wins\"': ' 55'})]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader.load()"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}