僅當使用 Python 和 Faker 的上一列中存在某個值時，如何將數據添加到列中？

Question

我對 Python 還很陌生，甚至不知道谷歌要做什么。 我要做的是創建一個 Pandas DataFrame 使用 Faker 填充虛假數據。 我遇到的問題是每一列都在一個孤島中生成假數據。 我希望能夠根據先前列中存在的內容創建虛假數據。

所以在下面的示例中，我有pc_type ["PC", "Apple]從那里我有操作系統，選項是 Windows 10、Windows 11 和 MacOS。現在我只想要pc_type = "Apple"列填充 MacOS 的值。然后對於類型為 PC 的所有內容，它是 50% Windows 10 和 50% Windows 11。

我將如何編寫此代碼，以便在 function 主體中我可以清楚地區分這種區別並且結果將反映這一點？

from faker import Faker
from faker.providers import BaseProvider, DynamicProvider
import numpy as np
import pandas as pd
from datetime import datetime
import random

pc_type = ['PC', 'Apple']
fake = Faker()


def create_data(x):
    project_data = {}
    for i in range(0, x):
        project_data[i] = {}
        project_data[i]['Name'] = fake.name()
        project_data[i]['PC Type'] = fake.random_element(pc_type)
        project_data[i]['With Windows 10'] = fake.boolean(chance_of_getting_true=25)
        project_data[i]['With Windows 11 '] = fake.boolean(chance_of_getting_true=25)
        project_data[i]['With MacOS'] = fake.boolean(chance_of_getting_true=50)

    return project_data


df = pd.DataFrame(create_data(10)).transpose()
df

Answer 1

要獲得一致的值，您可以使用以下內容：

from faker import Faker
import pandas as pd
import numpy as np


def create_data(x):
    pc_type = ['PC', 'Apple']
    fake = Faker()
    data = {'Name': [fake.name() for _ in range(x)],
            'PC Type': np.random.choice(pc_type, x)}
    df = pd.DataFrame(data)
    df['With MacOS'] = df['PC Type'] == 'Apple'

    pc = df['PC Type'] == 'PC'
    w10 = np.random.choice([True, False], len(df), p=(0.5, 0.5))
    df['With Windows 10'] = pc & w10
    df['With Windows 11'] = pc & ~w10

    return df

df = create_data(10)

Output：

>>> df
                Name PC Type  With MacOS  With Windows 10  With Windows 11
0     Charles Dawson      PC       False             True            False
1  Patricia Bautista      PC       False            False             True
2         Ruth Clark      PC       False             True            False
3       Justin Lopez      PC       False             True            False
4      Grace Russell      PC       False             True            False
5         Grant Moss      PC       False             True            False
6           Tracy Ho   Apple        True            False            False
7    Connie Mitchell   Apple        True            False            False
8  Catherine Nichols   Apple        True            False            False
9   Nathaniel Bryant      PC       False            False             True

Answer 2

我會稍微改變方法並生成一個列OS 。 如果需要，您可以將此列轉換為With MacOS等。

使用這種方法，更容易在 Windows 中獲得 0.5 / 0.5 拆分：

from faker import Faker
from faker.providers import BaseProvider, DynamicProvider
import numpy as np
import pandas as pd
from datetime import datetime
import random
from collections import OrderedDict

pc_type = ['PC', 'Apple']
wos_type = OrderedDict([('With Windows 10', 0.5), ('With Windows 11', 0.5)])
fake = Faker()

def create_data(x):
    project_data = {}
    for i in range(x):
        project_data[i] = {}
        project_data[i]['Name'] = fake.name()
        project_data[i]['PC Type'] = fake.random_element(pc_type)
        if project_data[i]['PC Type'] == 'PC':
            project_data[i]['OS'] = fake.random_element(elements = wos_type)
        else:
            project_data[i]['OS'] = 'MacOS'

    return project_data


df = pd.DataFrame(create_data(10)).transpose()
df

Output

                     Name PC Type               OS
0         Nicholas Walker   Apple            MacOS
1               Eric Hull      PC  With Windows 10
2       Veronica Gonzales      PC  With Windows 11
3  Mrs. Krista Richardson   Apple            MacOS
4              Anne Craig      PC  With Windows 10
5            Joseph Hayes      PC  With Windows 10
6             Mary Nelson   Apple            MacOS
7               Jill Hunt   Apple            MacOS
8             Mark Taylor      PC  With Windows 11
9           Kyle Thompson      PC  With Windows 10