熊猫应用功能行明智地花了太长的时间，下面的代码有什么替代方法[重复]

Question

我有一个数据框和如下所示的大函数，我想将norm_group函数应用于数据框列，但使用apply命令花费了太多时间。有没有办法缩短这段代码的时间？目前每个环路需要24.4秒。

import pandas as pd
import numpy as np

np.random.seed(1234)
n = 1500000

df = pd.DataFrame()
df['group'] = np.random.randint(1700, size=n)
df['ID'] = np.random.randint(5, size=n)
df['s_count'] = np.random.randint(5, size=n)
df['p_count'] = np.random.randint(5, size=n)
df['d_count'] = np.random.randint(5, size=n)
df['Total'] = np.random.randint(400, size=n)
df['Normalized_total'] = df.groupby('group')['Total'].apply(lambda x: (x-x.min())/(x.max()- x.min()))
df['Normalized_total'] = df['Normalized_total'].apply(lambda x:round(x,2))

def norm_group(a,b,c,d,e):
if a >= 0.7 and b >=1000 and c >2:
    return "Both High "
elif a >= 0.7 and b >=1000 and c < 2:
    return "High and C Low"
elif a >= 0.4 and b >=500 and d > 2:
    return "Medium and D High"
elif a >= 0.4 and b >=500 and d < 2:
    return "Medium and D Low"
elif a >= 0.4 and b >=500 and e > 2:
    return "Medium and E High"
elif a >= 0.4 and b >=500 and e < 2:
    return "Medium and E Low"
else:
    return "Low"

%timeit df['Categery'] = df.apply(lambda x:norm_group(a=x['Normalized_total'],b=x['group']), axis=1)

每循环24.4 s±551 ms（平均±std. dev.7次运行，每次1次循环）

在我的原始数据框中有多个文本列，我希望应用类似的函数，与此函数相比，它需要花费更多的时间。

谢谢

Answer 1

您可以使用np进行矢量化。选择：

df['Category'] = np.select((df['Normalized_total'].ge(0.7) & df['group'].ge(1000),
                            df['Normalized_total'].ge(0.4) & df['group'].ge(500)),
                           ('High', 'Medium'), default='Low'
                          )

性能：

255 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)