How to load CSVs
Acomma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
LangChain implements aCSV Loader that will load CSV files into a sequence ofDocument objects. Each row of the CSV file is translated to one document.
from langchain_community.document_loaders.csv_loaderimport CSVLoader
file_path="../integrations/document_loaders/example_data/mlb_teams_2012.csv"
loader= CSVLoader(file_path=file_path)
data= loader.load()
for recordin data[:2]:
print(record)
page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98' metadata={'source': '../../../docs/integrations/document_loaders/example_data/mlb_teams_2012.csv', 'row': 0}
page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97' metadata={'source': '../../../docs/integrations/document_loaders/example_data/mlb_teams_2012.csv', 'row': 1}
Customizing the CSV parsing and loading
CSVLoader will accept acsv_args kwarg that supports customization of arguments passed to Python'scsv.DictReader. See thecsv module documentation for more information of what csv args are supported.
loader= CSVLoader(
file_path=file_path,
csv_args={
"delimiter":",",
"quotechar":'"',
"fieldnames":["MLB Team","Payroll in millions","Wins"],
},
)
data= loader.load()
for recordin data[:2]:
print(record)
page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"' metadata={'source': '../../../docs/integrations/document_loaders/example_data/mlb_teams_2012.csv', 'row': 0}
page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98' metadata={'source': '../../../docs/integrations/document_loaders/example_data/mlb_teams_2012.csv', 'row': 1}
Specify a column to identify the document source
The"source" key onDocument metadata can be set using a column of the CSV. Use thesource_column argument to specify a source for the document created from each row. Otherwisefile_path will be used as the source for all documents created from the CSV file.
This is useful when using documents loaded from CSV files for chains that answer questions using sources.
loader= CSVLoader(file_path=file_path, source_column="Team")
data= loader.load()
for recordin data[:2]:
print(record)
page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98' metadata={'source': 'Nationals', 'row': 0}
page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97' metadata={'source': 'Reds', 'row': 1}
Load from a string
Python'stempfile can be used when working with CSV strings directly.
import tempfile
from ioimport StringIO
string_data="""
"Team", "Payroll (millions)", "Wins"
"Nationals", 81.34, 98
"Reds", 82.20, 97
"Yankees", 197.96, 95
"Giants", 117.62, 94
""".strip()
with tempfile.NamedTemporaryFile(delete=False, mode="w+")as temp_file:
temp_file.write(string_data)
temp_file_path= temp_file.name
loader= CSVLoader(file_path=temp_file_path)
data= loader.load()
for recordin data[:2]:
print(record)
page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98' metadata={'source': 'Nationals', 'row': 0}
page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97' metadata={'source': 'Reds', 'row': 1}