How to drop duplicated rows using pandas in a big

问题:

I have a csv file that too big to load to memory.I need to drop duplicated rows of the file.So I follow this way:

chunker = pd.read_table(AUTHORS_PATH, names=['Author ID', 'Author name'],      encoding='utf-8', chunksize=10000000)

for chunk in chunker:
    chunk.drop_duplicates(['Author ID'])

But if duplicated rows distribute in different chunk seems like above script can't get the expected results.

Is there any better way？

回答1:

You could try something like this.

First, create your chunker.

chunker = pd.read_table(AUTHORS_PATH, names=['Author ID', 'Author name'], encoding='utf-8', chunksize=10000000)

Now create a set of ids:

ids = set()

Now iterate over the chunks:

for chunk in chunker:
    chunk.drop_duplicates(['Author ID'])

However, now, within the body of the loop, drop also ids already in the set of known ids:

    chunk = chunk[~chunk['Author ID'].isin(ids)]

Finally, still within the body of the loop, add the new ids

    ids.update(chunk['Author ID'].values)

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If ids is too large to fit into main memory, you might need to use some disk-based database.