一个纯粹的 pandas 解决方案是使用sample：

out = pd.DataFrame(
    {g: [True]*c + [np.nan]*(R-c) for g, c in df.to_numpy()}
).sample(frac=1)

输出：

print(out)

      A   B     C     D
0  True NaN  True   NaN
1   NaN NaN  True   NaN
2  True NaN   NaN   NaN
3   NaN NaN  True  True
4   NaN NaN  True   NaN

• 老答案：*

一个简单的方法是 * 引导 * 一个预空DataFrame，同时随机选择/拾取一个坐标[index, column]：

np.random.seed(0)

R = 5 # <-- rows

out = pd.DataFrame(
    np.nan, index=range(R), columns=list(df["group"])
)

for g, c in df.to_numpy():
    out.loc[np.random.choice(out.index, c, replace=False), g] = True

步骤：
1.从字典列表中定义DataFrame

1. r是最终DataFrame中的行数
   1.创建一个字典group_indexes，每个键是一个group名称，每个值是随机选择的唯一行索引。索引的数量是count和r中的最小值。
   1.创建空DataFrame df_empty，其中r行和列由唯一的group名称定义。
   1.遍历df_empty中的每一列。如果column name在group_indexes中，则它将列中那些行索引处的值设置为True。
2. df_filled是通过将df_empty中的所有其他非True值替换为NaN来创建的。

import pandas as pd
import numpy as np

# Step 1: initial DataFrame
inputs = [
    {"group": "A", "count": 2},
    {"group": "B", "count": 0}, 
    {"group": "C", "count": 4}, 
    {"group": "D", "count": 1}, 
]
df = pd.DataFrame(inputs)

# Step 2: Define the number of rows
r = 5

# Step 3: Create a dictionary with unique random indexes for each group
group_indexes = {group: np.random.choice(np.arange(r), min(count, r), replace=False) for group, count in zip(df['group'], df['count']) if count > 0}

# Step 4: Create an empty DataFrame with 'r' rows and columns defined by groups
df_empty = pd.DataFrame(index=np.arange(r), columns=df['group'])

# Step 5: Place 'True' in the DataFrame according to the indexes in group_indexes
for col in df_empty:
    if col in group_indexes:
        df_empty.loc[group_indexes[col], col] = True

# Step 6: Replace all other values in the DataFrame with NaN
df_filled = df_empty.where(df_empty, np.NaN)

这是另一个我认为更简单的方法：

import pandas as pd
import numpy as np

# initial DataFrame
inputs = [
    {"group": "A", "count": 2},
    {"group": "B", "count": 0}, 
    {"group": "C", "count": 4}, 
    {"group": "D", "count": 1}, 
]
df = pd.DataFrame(inputs)

r = 5  # specify number of rows here

# Create a DataFrame filled with NaN
df_filled = pd.DataFrame(index=np.arange(r), columns=df['group'])

# Randomly assign True values in each column based on its count
for index, row in df.iterrows():
    if row['count'] > 0:
        random_indexes = np.random.choice(df_filled.index, size=min(r, row['count']), replace=False)
        df_filled.loc[random_indexes, row['group']] = True

这首先初始化一个用NaN填充的DataFrame。然后，对于每个组，它根据组的计数随机选择唯一的索引，并将这些位置设置为True。