Python Pandas - 使用 to_sql 以块的形式写入大型数据帧

Question

I'm using Pandas' to_sql function to write to MySQL, which is timing out due to large frame size (1M rows, 20 columns).

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html

Is there a more official way to chunk through the data and write rows in blocks? I've written my own code, which seems to work. I'd prefer an official solution though. Thanks!

def write_to_db(engine, frame, table_name, chunk_size):

    start_index = 0
    end_index = chunk_size if chunk_size < len(frame) else len(frame)

    frame = frame.where(pd.notnull(frame), None)
    if_exists_param = 'replace'

    while start_index != end_index:
        print "Writing rows %s through %s" % (start_index, end_index)
        frame.iloc[start_index:end_index, :].to_sql(con=engine, name=table_name, if_exists=if_exists_param)
        if_exists_param = 'append'

        start_index = min(start_index + chunk_size, len(frame))
        end_index = min(end_index + chunk_size, len(frame))

engine = sqlalchemy.create_engine('mysql://...') #database details omited
write_to_db(engine, frame, 'retail_pendingcustomers', 20000)

Answer 1

Update: this functionality has been merged in pandas master and will be released in 0.15 (probably end of september), thanks to @artemyk! See https://github.com/pydata/pandas/pull/8062

So starting from 0.15, you can specify the chunksize argument and eg simply do:

df.to_sql('table', engine, chunksize=20000)

Answer 2

There is beautiful idiomatic function chunks provided in answer to this question

In your case you can use this function like this:

def chunks(l, n):
""" Yield successive n-sized chunks from l.
"""
    for i in xrange(0, len(l), n):
         yield l.iloc[i:i+n]

def write_to_db(engine, frame, table_name, chunk_size):
    for idx, chunk in enumerate(chunks(frame, chunk_size)):
        if idx == 0:
            if_exists_param = 'replace':
        else:
            if_exists_param = 'append'
        chunk.to_sql(con=engine, name=table_name, if_exists=if_exists_param)

Only drawback that it doesn't support slicing second index in iloc function.

Answer 3

Reading from one table and writing to other in chunks....

[myconn1 ---> Source Table],[myconn2----> Target Table],[ch= 10000]

for chunk in pd.read_sql_table(table_name=source, con=myconn1, chunksize=ch):
    chunk.to_sql(name=target, con=myconn2, if_exists="replace", index=False,
                 chunksize=ch)
    LOGGER.info(f"Done 1 chunk")