I have created a sqlite database using pandas df.to_sql however accessing it seems considerably slower than just reading in the 500mb csv file.
I need to:
Building on Chris Guarino's answer, here's some functions that provide a more general solution. See the example at the bottom for how to use them.
import re
def get_create_table_string(tablename, connection):
sql = """
select * from sqlite_master where name = "{}" and type = "table"
""".format(tablename)
result = connection.execute(sql)
create_table_string = result.fetchmany()[0][4]
return create_table_string
def add_pk_to_create_table_string(create_table_string, colname):
regex = "(\n.+{}[^,]+)(,)".format(colname)
return re.sub(regex, "\\1 PRIMARY KEY,", create_table_string, count=1)
def add_pk_to_sqlite_table(tablename, index_column, connection):
cts = get_create_table_string(tablename, connection)
cts = add_pk_to_create_table_string(cts, index_column)
template = """
BEGIN TRANSACTION;
ALTER TABLE {tablename} RENAME TO {tablename}_old_;
{cts};
INSERT INTO {tablename} SELECT * FROM {tablename}_old_;
DROP TABLE {tablename}_old_;
COMMIT TRANSACTION;
"""
create_and_drop_sql = template.format(tablename = tablename, cts = cts)
connection.executescript(create_and_drop_sql)
# Example:
# import pandas as pd
# import sqlite3
# df = pd.DataFrame({"a": [1,2,3], "b": [2,3,4]})
# con = sqlite3.connect("deleteme.db")
# df.to_sql("df", con, if_exists="replace")
# add_pk_to_sqlite_table("df", "index", con)
# r = con.execute("select sql from sqlite_master where name = 'df' and type = 'table'")
# print(r.fetchone()[0])
There is a gist of this code here