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This article introduces the relevant knowledge of "how to deal with large files on a single machine by python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

The following discussion is based on the assumption that a single row of data can be processed with zero correlation between rows.

Method 1:

Read to memory line by line using only Python built-in templates.

With yield, the benefit is to decouple read operations and processing operations:

Def python_read (filename):

With open (filename,'r',encoding='utf-8') as f:

While True:

Line = f.readline ()

If not line:

Return

Yield line

Read one row at a time, iterate row by line, and process data row by row

If _ _ name__ = ='_ _ main__':

G = python_read ('. / data/movies.dat')

For c in g:

Print (c)

# process c

Method 2:

The first method has some shortcomings, it is read line by line, and frequent IO operations slow down the processing efficiency. Is there a way for IO to read multiple lines at one time?

Pandas package read_csv function, as many as 38 parameters, very powerful.

When it comes to processing large files on a single machine, the chunksize parameter of read_csv can do this, which is set to 5, which means reading five lines at a time.

Def pandas_read (filename,sep=',',chunksize=5):

Reader = pd.read_csv (filename,sep,chunksize=chunksize)

While True:

Try:

Yield reader.get_chunk ()

Except StopIteration:

Print ('- Done---')

Break

Use the same as method 1:

If _ _ name__ = ='_ _ main__':

G = pandas_read ('. / data/movies.dat',sep= "::")

For c in g:

Print (c)

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