Pandas Sqlite read_sql_query (Too many columns)

Question

I have a dataset with 566 000 rows and 25 000 columns. I would like quickly reading sections of it with the sqlite3 python package. All the information I find is about how to do this with a few columns.

import sqlite3
import pandas as pd

# Create a new database file:
db = sqlite3.connect("db.sqlite")
# Load the CSV in chunks:
for c in pd.read_csv("dataframe.csv", # usecols = ['id_col'],
               chunksize=1000):
    # Append all rows to a new database table, which we name 'voters':
    c.to_sql('db', db, if_exists="append")

# Add an index on 'id_col':
db.execute("CREATE INDEX id_col ON db(id_col)") 
db.close()

Running this gives this error:

sqlite3.OperationalError: too many columns on HumanProtAtlas_scRNA

Running it with the commented out parameter usecols = ['id_col'] is successful and creates an indexed SQL table that can be read with the following:

def read_filtered_df(id):
    conn = sqlite3.connect("db.sqlite")
    q = f"SELECT * FROM db WHERE id_col = '{id}'"    # 
    return pd.read_sql_query(q, conn) 

The subsetting according to the desired id is correct. However, the read dataframe only contains the cl_id column and not the other 25k columns. Is there a way of using this index to read the original dataframe? Or of including all columns in the indexed sql table?

Kind regards,