使用Python中的多个内核遍历for循环

Question

I have the following code that is currently running like normal Python code:

def remove_missing_rows(app_list):
    print("########### Missing row removal ###########")
    missing_rows = []

''' Remove any row that has missing data in the name, id, or description column'''
    for row in app_list:
        if not row[1]:
            missing_rows.append(row)
            continue  # Continue loop to next row. No need to check more columns
        if not row[5]:
            missing_rows.append(row)
            continue  # Continue loop to next row. No need to check more columns
        if not row[4]:
            missing_rows.append(row)

    print("Number of missing entries: " + str(len(missing_rows)))  # 967 with current method

    # Remove the missing_rows from the original data
    app_list = [row for row in app_list if row not in missing_rows]
    return app_list

Now, after writing this for a smaller sample I wish to run this on a very large data set. To do this I thought it would be useful to utilise the multiple cores of my computer.

I'm struggling to implement this using the multiprocessing module though. Eg The idea I have is that Core 1 could work through the first half of the data set, while Core 2 would work through the last half. Etc. And do this in parallel. Is this possible?

Answer 1

This is probably not cpu bound. Try the code below.

I've used a set for very fast (hash-based) contains (you use it when you invoke if row not in missing_rows , and it's very slow for a long list).

If this is the csv module you're already holding tuples which are hashable so not many changes needed:

def remove_missing_rows(app_list):
    print("########### Missing row removal ###########")
    filterfunc = lambda row: not all([row[1], row[4], row[5]])
    missing_rows = set(filter(filterfunc, app_list))

    print("Number of missing entries: " + str(len(missing_rows)))  # 967 with current method

    # Remove the missing_rows from the original data
    # note: should be a lot faster with a set
    app_list = [row for row in app_list if row not in missing_rows]
    return app_list

Answer 2

You can use filter, to not iterate twice:

def remove_missing_rows(app_list):

    filter_func = lambda row: all((row[1], row[4], row[5]))

    return list(filter(filter_func, app_list))

But if you are doing data analysis, you probably should have a look into pandas. There you could do something like this:

import pandas as pd

df = pd.read_csv('your/csv/data/file', usecols=(1, 4, 5))
df = df.dropna() # remove missing values