将pandas数据框转换为内存中的文件状对象? [英] Turn pandas dataframe into a file-like object in memory?

问题描述

我每天将大约2-250万条记录加载到Postgres数据库中.

I am loading about 2 - 2.5 million records into a Postgres database every day.

然后我使用pd.read_sql读取此数据以将其转换为数据框，然后进行一些列操作和一些小的合并.我将此修改后的数据保存为单独的表，供其他人使用.

I then read this data with pd.read_sql to turn it into a dataframe and then I do some column manipulation and some minor merging. I am saving this modified data as a separate table for other people to use.

当我执行pd.to_sql时，它将永远花费.如果我保存一个csv文件并在Postgres中使用COPY FROM，则整个过程仅需几分钟，但服务器位于单独的计算机上，因此很难在其中传输文件.

When I do pd.to_sql it takes forever. If I save a csv file and use COPY FROM in Postgres, the whole thing only takes a few minutes but the server is on a separate machine and it is a pain to transfer files there.

使用psycopg2，看来我可以使用copy_expert来受益于批量复制，但仍然可以使用python.如果可能，我想避免编写实际的csv文件.我可以在内存中使用pandas数据框吗?

Using psycopg2, it looks like I can use copy_expert to benefit from the bulk copying, but still use python. I want to, if possible, avoid writing an actual csv file. Can I do this in memory with a pandas dataframe?

这是我的熊猫代码示例.我想添加copy_expert或其他一些东西，以便在可能的情况下更快地保存此数据.

Here is an example of my pandas code. I would like to add the copy_expert or something to make saving this data much faster if possible.

    for date in required_date_range:
        df = pd.read_sql(sql=query, con=pg_engine, params={'x' : date})
        ...
        do stuff to the columns
        ...
        df.to_sql('table_name', pg_engine, index=False, if_exists='append',  dtype=final_table_dtypes)

有人可以帮我提供示例代码吗?我宁愿仍然使用熊猫，最好在内存中使用它.如果没有，我将只写一个csv临时文件，然后这样做.

Can someone help me with example code? I would prefer to use pandas still and it would be nice to do it in memory. If not, I will just write a csv temporary file and do it that way.

编辑-这是我最后的有效代码.每个日期只需要几百秒(几百万行)，而不是几个小时.

Edit- here is my final code which works. It only takes a couple of hundred seconds per date (millions of rows) instead of a couple of hours.

to_sql ="使用CSV头从STDIN复制％s""

to_sql = """COPY %s FROM STDIN WITH CSV HEADER"""

def process_file(conn, table_name, file_object):
    fake_conn = cms_dtypes.pg_engine.raw_connection()
    fake_cur = fake_conn.cursor()
    fake_cur.copy_expert(sql=to_sql % table_name, file=file_object)
    fake_conn.commit()
    fake_cur.close()


#after doing stuff to the dataframe
    s_buf = io.StringIO()
    df.to_csv(s_buf) 
    process_file(cms_dtypes.pg_engine, 'fact_cms_employee', s_buf)

推荐答案

Python模块io(

Python module io(docs) has necessary tools for file-like objects.

import io

# text buffer
s_buf = io.StringIO()

# saving a data frame to a buffer (same as with a regular file):
df.to_csv(s_buf)

编辑. (我忘了)为了以后再从缓冲区读取，它的位置应该设置为开头:

Edit. (I forgot) In order to read from the buffer afterwards, its position should be set to the beginning:

s_buf.seek(0)

我不熟悉psycopg2，但根据 docs copy_expert和copy_from都可以使用，例如:

I'm not familiar with psycopg2 but according to docs both copy_expert and copy_from can be used, for example:

cur.copy_from(s_buf, table)

(对于Python 2，请参见 StringIO .)

(For Python 2, see StringIO.)

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