pandas to sql slow

收集关于pandas to sql 慢的一些讨论：
https://github.com/pandas-dev/pandas/issues/15276

https://stackoverflow.com/questions/29706278/python-pandas-to-sql-with-sqlalchemy-how-to-speed-up-exporting-to-ms-sql

from sqlalchemy import create_engine

engine = create_engine(your_options)
data_frame.to_sql(table_name, engine, method="multi")

https://stackoverflow.com/questions/48006551/speeding-up-pandas-dataframe-to-sql-with-fast-executemany-of-pyodbc

http://odo.pydata.org/en/latest/perf.html

mport pandas as pd
from sqlalchemy import create_engine, MetaData, Table, select
from StringIO import StringIO

ServerName = "myserver"
Database = "mydatabase"
TableName = "mytable"

engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database) #don't forget to add a password if needed

my_data_frame.head(0).to_sql(TableName, engine, if_exists='replace', index=False)  # create an empty table - just for structure
conn = engine.raw_connection()
cur = conn.cursor()
output = StringIO()
my_data_frame.to_csv(output, sep='\t', header=False, index=False) # a CSV that will be used for the bulk load
output.seek(0)
cur.copy_from(output, TableName, null="")  # null values become ''
conn.commit()
conn.close()
cur.close()

https://stackoverflow.com/questions/29706278/python-pandas-to-sql-with-sqlalchemy-how-to-speed-up-exporting-to-ms-sql