Goal: Economics

本文整理了2016年及2017年的经济学、计算机科学与信息技术领域的核心书籍与资源,涵盖从基础原理到策略思维、经济投资等多个方面,为读者提供深入学习与实践的指引。

2016

  • Foundational Theory of Economics
  • Critical Theory for Political Planning for Economics
  • Economics Overview of USA
  • Economics overview of CN

2017

  • Economics Ideas and Politics
  • Individual Perspective and Strategies
  • Individual Economic Plan and Practice
  • Economic Investment and Win

Datas

-《经济学原理》(曼昆) (英文版)
-《经济学》(萨谬尔森) (英文版)
-《国际经济学》(克鲁格曼) (英文版)
-《货币金融学》(米什金) (英文版)
-《自由,市场与国家》(布坎南) (英文版)
-《国富论》(亚当斯密) (英文版)
-《定位》(杰克 特劳特) (英文版)
-《策略思维》(阿维纳什.K) (英文版)
-《致命的自负》 (英文版)
- Strategies and Game:Theory and practice
- The Worldly Philosophers: The Lives, Times And Ideas Of The Great Economic Thinkers
- The Economics Anti-textbook: A Critical Thinker's Guide to Microeconomics
- A Critique of Political Economy
- Development as Freedom
- Human Action: A Treatise on Economics
- Theory of Games and Economic Behavior
- Individualism and Economic Order
- The General Theory of Employment, Interest, and Money
- The Shock Doctrine: The Rise of Disaster Capitalism

- Defending the Undefendable

@s, 2016-01-29

-Principles of Economics (by Mankiw 5th ed)
-国富论 (英文版)
-致命的自负 (英文版)
-Defending the Undefendable

Mars

代码import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import LinearRegression def pred_house_price(): plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['font.size'] = 12 y = np.asarray([6450, 7450, 8450, 9450, 11450, 15450, 18450]) x = np.asarray([150, 200, 250, 300, 350, 400, 600] ) plt.xlabel('面积(平方英尺)') plt.ylabel('售价(美元)') clf = LinearRegression() x = x.reshape(len(x), 1)#①为什么要调整维度? clf.fit(x, y) pre = clf.predict(x) plt.scatter(x, y, s=50) #②散点图,什么数据? plt.plot(x, pre, 'r-', linewidth=2)#③ 直线,什么数据? plt.show()报错:Traceback (most recent call last): File "C:\Users\Administrator\PycharmProjects\pythonProject1\exp4.1.py", line 21, in <module> boston = datasets.load_boston() ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Administrator\anaconda3\Lib\site-packages\sklearn\datasets\__init__.py", line 157, in __getattr__ raise ImportError(msg) ImportError: `load_boston` has been removed from scikit-learn since version 1.2. The Boston housing prices dataset has an ethical problem: as investigated in [1], the authors of this dataset engineered a non-invertible variable "B" assuming that racial self-segregation had a positive impact on house prices [2]. Furthermore the goal of the research that led to the creation of this dataset was to study the impact of air quality but it did not give adequate demonstration of the validity of this assumption. The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning. In this special case, you can fetch the dataset from the original source:: import pandas as pd import numpy as np data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] Alternative datasets include the California housing dataset and the Ames housing dataset. You can load the datasets as follows:: from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() for the California housing dataset and:: from sklearn.datasets import fetch_openml housing = fetch_openml(name="house_prices", as_frame=True) for the Ames housing dataset. [1] M Carlisle. "Racist data destruction?" <https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8> [2] Harrison Jr, David, and Daniel L. Rubinfeld. "Hedonic housing prices and the demand for clean air." Journal of environmental economics and management 5.1 (1978): 81-102. <https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>
03-14
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值