Question

我希望以下函数为数据帧中的每一行返回一个不同的数字，但每次该函数运行时都返回相同的数字。

谢谢。

def inc14(p):
if p==1:
    return random.randint(1,2000)
elif p==2:
    return random.randint(2001,3000)
elif p==3:
    return random.randint(3001,4000)
elif p==4:
    return random.randint(4001,5000)
elif p==5:
    return random.randint(5001,7000)
elif p==6:
    return random.randint(7001,9000)
elif p==7:
    return random.randint(9001,12000)
elif p==8:
    return random.randint(12001,15000)
elif p==9:
    return random.randint(15001,20000)
elif p==10:
    return random.randint(20001,40000)
elif p==11:
    return 0.01
else:
    return np.NaN

data['inc_cont14']=data['inc14'].apply(inc14)

Answer 1

随机性只是随机的，因为种子每次都会变化。如果您设置种子，则由于您从同一种子开始撒酸，每次都会得到相同的结果。

import random


def inc14(p):
    random.seed(10)
    if p == 1:
        return random.randint(1, 2000)
    elif p == 2:
        return random.randint(2001, 3000)
    elif p == 3:
        return random.randint(3001, 4000)
    elif p == 4:
        return random.randint(4001, 5000)
    elif p == 5:
        return random.randint(5001, 7000)
    elif p == 6:
        return random.randint(7001, 9000)
    elif p == 7:
        return random.randint(9001, 12000)
    elif p == 8:
        return random.randint(12001, 15000)
    elif p == 9:
        return random.randint(15001, 20000)
    elif p == 10:
        return random.randint(20001, 40000)
    elif p == 11:
        return 0.01
    else:
        return None
        return np.NaN


for _  in range(10):
    print(inc14(4), inc14(7))

输出

Answer 2

定义的范围无关紧要：

如果定义的范围无关紧要，请参见下面的示例：

import random
import pandas as pd

random.seed(42) # Seed is here to always produce the same numbers

data = {'Name':['Tom', 'nick', 'krish', 'jack'], 'Age':[20, 21, 19, 18]}
df = pd.DataFrame(data)  #create a dummy dataframe

# The dataframe has 4 rows. So we need 4 random numbers.
# If we want to generate 4 random numbers, without duplicates we can use random.sample
# In this example we sample 4 random number in the range of 0-399
range_multiplier = 100
df['Random'] = random.sample(range(len(df.index)*range_multiplier), len(df.index))

print(df)

输出：

    Name  Age  Random
0    Tom   20     327
1   nick   21      57
2  krish   19      12
3   jack   18     379

如果您使用的种子与我使用的种子相同，则可以运行相同的代码，并且将获得与我相同的随机数。

定义的范围很重要：

如果您在此需要此范围，则该新功能会短很多，但是您必须准备所有数字。

random.seed(42) # Seed is here to always produce the same numbers

# for all p(1-10) and their ranges (1-2000, 2001-3000, 3001-4000,...) 
# we generate a dictionary with p as the key 
# and as value a list of all numbers in the defined range
# without duplicates with random.sample
p_numbers = {
    1: random.sample(range(1, 2001), 2000),
    2: random.sample(range(2001, 3001), 1000),
    ...
    10: random.sample(range(20001,40001), 20000)
}

def inc14(p,p_numbers):
    if p >= 1 and p<=10:
        # take the first element of the number and remove it
        # from the list (to avoid taking it again)
        return p_numbers[p].pop(0) 
    elif p == 11:
        return 0.01
    else:
        return np.nan

data['inc_cont14']=data['inc14'].apply(inc14,p_numbers)

我们再次需要种子来避免重复。

我们用其p的可用数字创建一个字典。如果p在1到10之间，我们从字典中取出数字，然后从那里删除，以免两次。

随机数-每次运行时相同的数字，每行的数字不同

2 个答案: