- 学术相关
- Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects
- Customer voice on two-sided platforms: the effect of surge pricing on customer complaints
- Examining the replicability of online experiments selected by a decision market
- Decision-aware learning for optimizing health supply chains
- Optimal comprehensible targeting
- 技术技巧
学术相关
Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects1
Customer voice on two-sided platforms: the effect of surge pricing on customer complaints2
Examining the replicability of online experiments selected by a decision market3
Decision-aware learning for optimizing health supply chains4
Optimal comprehensible targeting5
技术技巧
Python丨云朵君： zip() 函数玩出花
列表
合并/拆分列表
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names = ["Alice", "Bob", "Charlie"]
ages = [25, 35, 29]
scores = [102, 382, 100]
combined = list(zip(names, ages, scores))
print(combined)
# [('Alice', 25, 102), ('Bob', 35, 382), ('Charlie', 29, 100)]
将配对数据拆分成单独列表。
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combined = [('Alice', 25), ('Bob', 30), ('Charlie', 35)]
names, ages = zip(*combined)
迭代列表
同时遍历多个列表。
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subjects = ['Math', 'Science', 'English']
scores = [88, 92, 85]
for subject, score in zip(subjects, scores):
print(f"{subject}: {score}")
当列表不同时，zip会在最短列表结束处停止。
比较列表元素
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list1 = [1, 2, 3]
list2 = [1, 4, 3]
comparison = [a == b for a, b in zip(list1, list2)]
字典
创建字典。
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keys = ['name', 'age', 'city']
values = ['Alice', 25, 'New York']
dictionary = dict(zip(keys, values))
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Xie, T., & Wang, Z. (2024). Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects. Operations Research, opre.2021.0645. https://doi.org/10.1287/opre.2021.0645 ↩
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Xu, L., Wei, Y. “Max,” & Zhu, Y. (2024). Customer voice on two-sided platforms: The effect of surge pricing on customer complaints. Management Science, mnsc.2024.4992. https://doi.org/10.1287/mnsc.2024.4992 ↩
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Holzmeister, F., Johannesson, M., Camerer, C. F., Chen, Y., Ho, T.-H., Hoogeveen, S., Huber, J., Imai, N., Imai, T., Jin, L., Kirchler, M., Ly, A., Mandl, B., Manfredi, D., Nave, G., Nosek, B. A., Pfeiffer, T., Sarafoglou, A., Schwaiger, R., … Dreber, A. (2024). Examining the replicability of online experiments selected by a decision market. Nature Human Behaviour, 1–15. https://doi.org/10.1038/s41562-024-02062-9 ↩
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Chung, T.-H., Rostami, V., Bastani, H., & Bastani, O. (2022). Decision-aware learning for optimizing health supply chains (No. arXiv:2211.08507). arXiv. https://doi.org/10.48550/arXiv.2211.08507 ↩
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Zhang, W. W. (n.d.). Optimal comprehensible targeting [University of Chicago]. https://doi.org/10.6082/uchicago.12376 ↩