最大限度地减少在大型 CSV 文件中搜索 python 的时间

Question

I have a CSV file with about 700 or so rows and 8 columns, the last column however, has a very big block of text (with enough for multiple long paragraphs inside each).

I'd like to implement through python a text-search function that gives me back all the lines that have text that matches from inside the data from the 8th column (meaning it'd need to go through the whole thing).

What could possibly be the quickest way to approach this and minimise search-time?

Answer 1

You could dump your csv file into an sqlite database and use sqlite's full text search capabilities to do the search for you.

This example code shows how it could be done. There are a few things to be aware of:

• it assumes that the csv file has a header row, and that the values of the headers will make legal column names in sqlite. If this isn't the case, you'll need to quote them (or just use generic names like "col1", "col2" etc).
• it searches all columns in the csv; if that's undesirable, filter out the other columns (and header values) before creating the SQL statements.
• If you want to be able to match the results to rows in the csv file, you'll need create a column that contains the line number.

import csv
import sqlite3
import sys


def create_table(conn, headers, name='mytable'):
    cols = ', '.join([x.strip() for x in headers])
    stmt = f"""CREATE VIRTUAL TABLE {name} USING fts5({cols})"""
    with conn:
        conn.execute(stmt)
    return


def populate_table(conn, reader, ncols, name='mytable'):
    placeholders = ', '.join(['?'] * ncols)
    stmt = f"""INSERT INTO {name}
    VALUES ({placeholders})
    """
    with conn:
        conn.executemany(stmt, reader)
    return


def search(conn, term, headers, name='mytable'):
    cols = ', '.join([x.strip() for x in headers])
    stmt = f"""SELECT {cols}
    FROM {name}
    WHERE {name} MATCH ?
    """
    with conn:
        cursor = conn.cursor()
        cursor.execute(stmt, (term,))
        result = cursor.fetchall()
    return result


def main(path, term):
    result = 'NO RESULT SET'
    try:
        # Create an in-memory database.
        conn = sqlite3.connect(':memory:')
        with open(path, 'r') as f:
            reader = csv.reader(f)
            # Assume headers are in the first row
            headers = next(reader)
            create_table(conn, headers)
            ncols = len(headers)
            populate_table(conn, reader, ncols)
        result = search(conn, term, headers)
    finally:
        conn.close()
    return result


if __name__ == '__main__':
    print(main(*sys.argv[1:]))