如何在Pandas read_csv函数的负载中过滤行? [英] How can I filter lines on load in Pandas read_csv function?

问题描述

如何使用熊猫过滤CSV的哪些行加载到内存中?这似乎是一个应该在read_csv中找到的选项.我想念什么吗?

How can I filter which lines of a CSV to be loaded into memory using pandas? This seems like an option that one should find in read_csv. Am I missing something?

示例:我们有一个带时间戳列的CSV，我们只想加载时间戳大于给定常量的行.

Example: we've a CSV with a timestamp column and we'd like to load just the lines that with a timestamp greater than a given constant.

推荐答案

在将CSV文件加载到熊猫对象之前，没有选项可以过滤行.

There isn't an option to filter the rows before the CSV file is loaded into a pandas object.

您可以加载文件，然后使用df[df['field'] > constant]进行过滤，或者如果您有一个非常大的文件，并且担心内存用完，那么在连接文件块时，使用迭代器并应用过滤器，例如:

You can either load the file and then filter using df[df['field'] > constant], or if you have a very large file and you are worried about memory running out, then use an iterator and apply the filter as you concatenate chunks of your file e.g.:

import pandas as pd
iter_csv = pd.read_csv('file.csv', iterator=True, chunksize=1000)
df = pd.concat([chunk[chunk['field'] > constant] for chunk in iter_csv])

您可以更改chunksize以适合您的可用内存.请参见此处更多细节.

You can vary the chunksize to suit your available memory. See here for more details.

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