如何在 python 中使用模拟数据创建数据框答案

【问题标题】：How to create a dataframe with simulated data in python如何在 python 中使用模拟数据创建数据框
【发布时间】：2020-07-28 12:11:25
【问题描述】：

我有示例架构，它包含 12 列，每列都有特定的类别。现在我需要将这些数据模拟成一个大约 1000 行的数据框。我该怎么办？

我使用下面的代码为每一列生成数据

      Location = ['USA','India','Prague','Berlin','Dubai','Indonesia','Vienna']
      Location = random.choice(Location)

      Age = ['Under 18','Between 18 and 64','65 and older']
      Age = random.choice(Age)

      Gender = ['Female','Male','Other']
      Gender = random.choice(Gender)

等等

我需要如下输出

       Location        Age          Gender
       Dubai           below 18     Female
       India           65 and older Male

。 . . .

【问题讨论】：

标签： python python-3.x pandas dataframe random

【解决方案1】：

您可以为数据框中所需的行数创建一个 for 循环，然后生成字典列表。使用字典列表生成数据框。

In [16]: for i in range(5):
    ...:     k={}
    ...:     loc = random.choice(Location)
    ...:     age = random.choice(Age)
    ...:     gen = random.choice(Gender)
    ...:     k = {'Location':loc,'Age':age, 'Gender':gen}
    ...:     list2.append(k)
    ...:

In [17]: import pandas as pd

In [18]: df = pd.DataFrame(list2)

In [19]: df
Out[19]:
                 Age Gender   Location
0  Between 18 and 64  Other     Berlin
1       65 and older  Other        USA
2       65 and older   Male      Dubai
3  Between 18 and 64   Male      Dubai
4  Between 18 and 64   Male  Indonesia

【讨论】：

【解决方案2】：

您可以使用np.random.choice 逐一创建每一列：

df = pd.DataFrame()                                                                                                                                                                     
N = 1000                                                                                                                                                                                
df["Location"] = np.random.choice(Location, size=N)                                                                                                                                     
df["Age"] = np.random.choice(Age, size=N)                                                                                                                                               
df["Gender"] = np.random.choice(Gender, size=N)

或者使用列表推导来做到这一点：

column_to_choice = {"Location": Location, "Age": Age, "Gender": Gender}

df = pd.DataFrame(
    [np.random.choice(column_to_choice[c], 100) for c in column_to_choice]
).T

df.columns = list(column_to_choice.keys())

结果：

>>> print(df.head())                                                                                                                                                                              
    Location                Age  Gender
0      India       65 and older  Female
1     Berlin  Between 18 and 64  Female
2        USA  Between 18 and 64    Male
3  Indonesia           Under 18    Male
4      Dubai           Under 18   Other

【讨论】：