有效地选择与Pandas DataFrame中的几个值之一匹配的行 [英] Efficiently select rows that match one of several values in Pandas DataFrame

问题描述

在Pandas DataFrame中提供数据，如下所示:

Given data in a Pandas DataFrame like the following:

Name     Amount
---------------
Alice       100
Bob          50
Charlie     200
Alice        30
Charlie      10

我想选择Name是集合{Alice, Bob}

Name     Amount
---------------
Alice       100
Bob          50
Alice        30

问题

在Pandas中执行此操作的有效方法是什么?

Question

What is an efficient way to do this in Pandas?

我看到的选项

1. 遍历行，使用Python处理逻辑

2. 选择并合并许多类似于以下内容的语句

1. Loop through rows, handling the logic with Python

2. Select and merge many statements like the following

merge(df[df.name = specific_name] for specific_name in names) # something like this

执行某种联接

Perform some sort of join

这里的性能折衷是什么?一种解决方案何时比其他解决方案更好?我缺少什么解决方案?

What are the performance trade-offs here? When is one solution better than the others? What solutions am I missing?

虽然上面的示例使用字符串，但我的实际工作是使用数百万行中10-100个整数的匹配项，因此快速的NumPy操作可能很重要.

While the example above uses strings my actual job uses matches on 10-100 integers over millions of rows and so fast NumPy operations may be relevant.

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