您可以单独计算累计和并绘制结果：

df['cumsum'] = df.groupby('hue_val').value.transform('cumsum')
sns.lineplot(x="idx", y="cumsum", hue="hue_val", data=df)

给定OP Dataframe

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = {
    'idx': [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10],
    'hue_val': ["A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B","C","C","C","C","C","C","C","C","C","C",],
    'value': np.random.rand(30)
}

df = pd.DataFrame(data)

有两件事需要做：
1.计算每个hue_val的累积和
1.把它画出来

1.计算每个hue_val的累计和

为了计算累计和，可以使用pandas.DataFrame.groupby和pandas.Series.cumsum。根据OP的要求，使用变量column作为选择要考虑的列的方法，如下所示

column = 'value'
df['cum_sum'] = df.groupby('hue_val')[column].cumsum()

当使用Numpy生成一些dataframe值时，也可以使用它来计算pandas.DataFrame.apply和numpy.cumsum的cum sum，如下所示

df['cum_sum'] = df.groupby('hue_val')[column].apply(lambda x: np.cumsum(x))

2.画出来

然后可以用seaborn.lineplot绘制它，如下所示

sns.lineplot(data=df, x='idx', y='cum_sum', hue='hue_val')

注：

• 使用一个变量来指定列，如果 Dataframe 有更多的列，则会使其更加用户友好，如下面所示（OP关注的问题之一），因为只需将变量更改为，比方说value3

data = {
    'idx': [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10],
    'hue_val': ["A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B","C","C","C","C","C","C","C","C","C","C",],
    'value': np.random.rand(30),
    'value1': np.random.rand(30),
    'value2': np.random.rand(30),
    'value3': np.random.rand(30),
}

df = pd.DataFrame(data)

column = 'value3'
df['cum_sum'] = df.groupby('hue_val')[column].cumsum()

sns.lineplot(data=df, x='idx', y='cum_sum', hue='hue_val')

• 有强烈的意见使用.apply() .会推荐阅读此：When should I (not) want to use pandas apply() in my code?