How to load CSVs

A comma-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 a CSV Loader that will load CSV files into a sequence of Document objects. Each row of the CSV file is translated to one document.

from langchain_community.document_loaders.csv_loader import CSVLoader

file_path = (
    "../../../docs/integrations/document_loaders/example_data/mlb_teams_2012.csv"
)

loader = CSVLoader(file_path=file_path)
data = loader.load()

for record in data[:2]:
    print(record)

API Reference:

• CSVLoader

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 a csv_args kwarg that supports customization of arguments passed to Python's csv.DictReader. See the csv 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 record in 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 on Document metadata can be set using a column of the CSV. Use the source_column argument to specify a source for the document created from each row. Otherwise file_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 record in 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's tempfile can be used when working with CSV strings directly.

import tempfile
from io import 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)
loader.load()
for record in 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}