我有一个调查回复数据。它具有多个列，但其中一列是用户使用产品的年龄。另一列是用户在用于计算NPS的0-10点规模上给出的响应。
现在，我已经使用此响应变量计算了NPS，但我还想要的是Age变量如何影响NPS得分。任何可以帮助解释的可视化或模型。
NPS是根据我编写的以下函数计算的，该函数吸收所有用户的响应并找到Promoters_Proportion（9＆10）和Detractors_proportion（0-6）
def nps_score(responses):
"""Function to get the NPS score from the
Survey responses
"""
global sample_size,promoters_proportion,detractors_proportion,sample_NPS
sample_size = len(responses)
promoters_proportion = sum([1 for x in responses if x >=9])/sample_size
detractors_proportion= sum([1 for x in responses if x<=6])/sample_size
sample_NPS= promoters_proportion - detractors_proportion
print("Sample Net Promoter Score(NPS) is {} or {}%".
format(round(sample_NPS,2),round(sample_NPS*100,2)))
任何想法/帮助都值得赞赏。
下面的示例数据是年龄（以天为单位），用户的响应
array([['161.791365740741', 8.0],
['40.7387384259259', 7.0],
['1,289.49306712963', 8.0],
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