如何在不使用groupby()函数的情况下对数据帧的行进行分组？

import pandas as pd

# 定义一个自定义函数，根据行的某个特征值进行分组
def group_rows(row):
    if row['特征值'] == 'A':
        return 'Group A'
    elif row['特征值'] == 'B':
        return 'Group B'
    else:
        return 'Other'

# 创建数据帧
df = pd.DataFrame({'特征值': ['A', 'B', 'C', 'A', 'B', 'C'],
                   '数值': [1, 2, 3, 4, 5, 6]})

# 使用apply()函数将自定义函数应用到每一行
df['分组'] = df.apply(group_rows, axis=1)

# 打印结果
print(df)

  特征值  数值       分组
0   A   1  Group A
1   B   2  Group B
2   C   3    Other
3   A   4  Group A
4   B   5  Group B
5   C   6    Other

import pandas as pd

# 创建数据帧
df = pd.DataFrame({'数值': [1, 2, 3, 4, 5, 6]})

# 根据数值的范围进行分组
bins = [0, 2, 4, 6]
labels = ['Group A', 'Group B', 'Group C']
df['分组'] = pd.cut(df['数值'], bins=bins, labels=labels)

# 打印结果
print(df)

   数值       分组
0   1  Group A
1   2  Group A
2   3  Group B
3   4  Group B
4   5  Group C
5   6  Group C

import pandas as pd
import numpy as np

# 创建数据帧
df = pd.DataFrame({'数值': [1, 2, 3, 4, 5, 6]})

# 根据数值的范围进行分组
bins = [0, 2, 4, 6]
labels = ['Group A', 'Group B', 'Group C']
df['分组'] = labels[np.digitize(df['数值'], bins=bins) - 1]

# 打印结果
print(df)

   数值       分组
0   1  Group A
1   2  Group A
2   3  Group B
3   4  Group B
4   5  Group C
5   6  Group C