Pandas应用功能行的时间太长，有没有任何替代低于化学需氧量

Question

我有一个像下面这样的数据框和大函数，我想将norm\u group函数应用到数据框列，但使用apply命令会花费太多时间。有没有什么方法可以减少这个代码的时间？目前每个环路需要24.4秒。你知道吗

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)

24.4 s±551 ms/回路（7次运行的平均值±标准偏差，每次1回路）

我的原始数据框中有多个文本列，希望应用类似的函数，与此函数相比，这类函数需要花费更多的时间。你知道吗

谢谢

Answer 1

可以使用np.select矢量化：

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)