如何在读取大的csv文件时解决 pandas 的内存问题 [英] How to resolve memory issue of pandas while reading big csv files

问题描述

我有一个100GB的csv文件，其中包含数百万行.我需要一次在pandas数据框中读取10,000行，并将其分块写入SQL Server.

I have a 100GB csv file with millions of rows. I need to read, say, 10,000 rows at a time in pandas dataframe and write that to the SQL server in chunks.

我按照

I used chunksize as well as iteartor as suggested on http://pandas-docs.github.io/pandas-docs-travis/io.html#iterating-through-files-chunk-by-chunk, and have gone through many similar questions,but I am still getting the out of memory error.

您能建议一个代码来迭代读取pandas数据框中的很大的csv文件吗?

Can you suggest a code to read very big csv files in pandas dataframe iteratively?

推荐答案

演示:

for chunk in pd.read_csv(filename, chunksize=10**5):
    chunk.to_sql('table_name', conn, if_exists='append')

其中conn是一个SQLAlchemy引擎(由sqlalchemy.create_engine(...)创建)

where conn is a SQLAlchemy engine (created by sqlalchemy.create_engine(...))

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