加速 Pandas 应用功能 [英] Speeding up Pandas apply function

问题描述

对于一个相对较大的 Pandas DataFrame(几个 100k 行)，我想创建一个作为应用函数结果的系列.问题是这个函数不是很快，我希望它能以某种方式加速.

For a relatively big Pandas DataFrame (a few 100k rows), I'd like to create a series that is a result of an apply function. The problem is that the function is not very fast and I was hoping that it can be sped up somehow.

df = pd.DataFrame({
 'value-1': [1, 2, 3, 4, 5],
 'value-2': [0.1, 0.2, 0.3, 0.4, 0.5],
 'value-3': somenumbers...,
 'value-4': more numbers...,
 'choice-index': [1, 1, np.nan, 2, 1]
})

def func(row):
  i = row['choice-index']
  return np.nan if math.isnan(i) else row['value-%d' % i]

df['value'] = df.apply(func, axis=1, reduce=True)

# expected value = [1, 2, np.nan, 0.4, 5]

欢迎提出任何建议.

更新

通过预缓存选定的列可以实现非常小的加速(~1.1).func 将更改为:

A very small speedup (~1.1) can be achieved by pre-caching the selected columns. func would change to:

cached_columns = [None, 'value-1', 'value-2', 'value-3', 'value-4']
def func(row):
  i = row['choice-index']
  return np.nan if math.isnan(i) else row[cached_columns[i]]

但我希望有更大的加速...

But I was hoping for greater speedups...

推荐答案

我想我得到了一个很好的解决方案(加速 ~150).

I think I got a good solution (speedup ~150).

诀窍不是使用apply，而是进行智能选择.

The trick is not to use apply, but to do smart selections.

choice_indices = [1, 2, 3, 4]
for idx in choice_indices:
  mask = df['choice-index'] == idx
  result_column = 'value-%d' % (idx)
  df.loc[mask, 'value'] = df.loc[mask, result_column]

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