├── .gitignore ├── LICENSE ├── README.md ├── 시뮬레이션 데이터.ipynb └── 실무로 통하는 인과추론 with 파이썬 ├── 01장-인과추론 소개.ipynb ├── 02장-무작위 실험 및 기초 통계 리뷰.ipynb ├── 03장-그래프 인과모델.ipynb ├── 04장-유용한 선형회귀.ipynb ├── 05장-성향점수.ipynb ├── 06장-이질적 처치효과.ipynb ├── 07장-메타러너.ipynb ├── 08장-이중차분법.ipynb ├── 09장-통제집단합성법.ipynb ├── 10장-지역 실험과 스위치백 실험.ipynb ├── 11장-불응과 도구변수.ipynb └── data ├── book_cover.jpg ├── br_cities.csv ├── cross_sell_email.csv ├── daily_restaurant_sales.csv ├── discount_data.csv ├── email_obs_data.csv ├── email_rnd_data.csv ├── enem_scores.csv ├── interest_rate.csv ├── management_training.csv ├── offline_mkt_staggered.csv ├── online_classroom.csv ├── online_mkt.csv ├── online_mkt_cov.csv ├── prime_card.csv ├── prime_card_discontinuity.csv ├── rec_ab_test.csv ├── risk_data.csv ├── risk_data_rnd.csv ├── sb_exp_every.csv ├── sb_exp_opt.csv ├── short_offline_mkt_all_regions.csv ├── short_offline_mkt_south.csv ├── spend_data.csv ├── spend_data_rnd.csv ├── wage.csv └── xmas_sales.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | 132 | # Apple 133 | *.DS_Store 134 | 135 | # R 136 | .Rhistory 137 | 138 | # Custom 139 | 140 | build/* 141 | build_/* 142 | data/img/* -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Matheus Facure 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 『실무로 통하는 인과추론 with 파이썬』 2 | 3 | 이 저장소는 한빛출판사에서 출간한 한국어판 "실무로 통하는 인과추론 with 파이썬"에서 참조하는 각종 자료 및 소스 코드와 예제 데이터를 담고 있습니다. 이 책은 다음 서점에서 절찬리에 판매중입니다([교보문고](https://product.kyobobook.co.kr/detail/S000212577153), [예스24](https://www.yes24.com/Product/Goods/125196916), [알라딘](https://www.aladin.co.kr/shop/wproduct.aspx?ItemId=335177841), [한빛미디어](https://www.hanbit.co.kr/store/books/look.php?p_code=B6208936856)) 4 | 5 | ![img](./실무로%20통하는%20인과추론%20with%20파이썬/data/book_cover.jpg) 6 | 7 | # 책 소개 # 8 | 독자 여러분은 "상관관계는 인과관계가 아니다"라는 말을 많이 들어보셨죠? 이 책은 상관관계가 왜 인과관계와 다른지 그리고 데이터 과학자의 시각에서 인과추론의 기초부터 심화에 이르기까지의 내용을 다양한 실무 사례를 바탕으로 설명합니다. 나아가 인과추론 방법론에 그치지 않고 실무에서의 데이터 분석가 또는 데이터 과학자가 고민하는 수준의 실험 결과의 신뢰성 및 추론 부분까지 다루고 있습니다. 여러분이 인과추론을 처음 접하셨다면, 내용을 바탕으로 파이썬 실습 위주로 진행해볼 수 있습니다. 그리고 여러분이 데이터 분석가/과학자라면 사례를 중심으로 여러분의 도메인에 접목시켜 이론과 사례를 함께 학습할 수 있습니다. 9 | 10 | 11 | 주요 내용은 다음과 같습니다. 12 | - 인과추론의 기본 개념과 활용법 익히기 13 | - 인과추론과 편향의 관계 이해하기 14 | - 인과추론으로 비즈니스 문제 해결하기 15 | - 인과추론으로 고객을 시간에 따라 관찰하기 16 | - 인과효과가 실험 대상마다 다를 수 있는 이유 학습하기 17 | 18 | 19 | # 역자 소개 20 | ## [신진수](https://www.linkedin.com/in/jinsoo-shin-436060162/) 21 | 네오플을 거쳐 크래프톤의 데이터 분석가로 일하고 있습니다. 게임 업계에서 쌓은 커리어를 기반으로 <던전앤파이터>, <뉴스테이트 모바일>, <배틀그라운드 모바일> 등 다양한 장르의 게임에서 데이터 분석과 실험을 통해 유저 경험을 개선하는 데 기여했습니다. 비영리 데이터 사이언스 커뮤니티인 가짜연구소에서 인과추론팀을 운영 중입니다. 마테우스 파쿠레의 웹북 [「Causal Inference for The Brave and True」](https://github.com/CausalInferenceLab/Causal-Inference-with-Python)를 한국어로 번역하는 작업을 주도했습니다 ([블로그](https://jinsooshin.tistory.com/) / [Github](https://github.com/jsshin2022)) 22 | 23 | 24 | ## [가짜연구소 인과추론팀](https://pseudo-lab.com/6bbf03d9f11d4af687c0f03c6db39b1b) 25 | 가짜연구소 인과추론팀은 2022년부터 데이터를 통한 문제해결력을 높이고자 인과추론을 함께 학습하고 있습니다. 한국어 자료가 많지 않은 인과추론을 많은 분이 쉽게 접하실 수 있도록 기여하고자 하는 마음으로, 가짜연구소에서 인과추론 이야기와 실험 및 조직문화에 대한 이야기를 이어나가고 있습니다. 이 책의 번역 작업에는 인과추론팀 김소희, 김성수, 김상돈, 김준영, 남궁민상, 박시온, 최은희, 정호재, 홍성철이 함께 참여했습니다. 26 | 27 | # 감수자 소개 28 | ## [박지용](https://jiyong-park.github.io/) 29 | 미국 조지아대학교 테리 경영대학(Terry College of Business)에서 경영정보시스템 조교수로 일하고 있다. 디지털기술의 사회적, 환경적 영향에 대한 실증연구를 해오며 인과추론 방법론을 통해 사회현상과 기업활동에서의 원인과 결과를 분석하는 일들을 수행하고 있다. 인과추론의 저변을 확대하기 위해 매년 여름 를 조직해왔고, 유튜브 채널 <[인과추론의 데이터과학](https://www.youtube.com/@causaldatascience)>을 운영중이다. 30 | 31 | 32 | 33 | # 챕터별 자료 34 | 35 | ## 번역서 온보딩을 위한 강의 & 블로그 36 | > - [인과추론의 데이터과학 Youtube](https://www.youtube.com/@causaldatascience) 37 | > - [가짜연구소 인과추론팀 블로그](https://causalinferencelab.github.io/) 38 | > - [역자 블로그](https://jinsooshin.tistory.com/) 39 | 40 | 41 | ## 참고 도서 42 | > - Causal Inference for the Brave and True ([원서](https://github.com/matheusfacure/python-causality-handbook) / [번역서](https://github.com/CausalInferenceLab/Causal-Inference-with-Python)) 43 | > - [Causal Inference Stanford STATS 361 강의 노트](https://web.stanford.edu/~swager/stats361.pdf) 44 | > - [Causal Inference for Statistics, Social, and Biomedical Sciences](https://www.amazon.com/Causal-Inference-Statistics-Biomedical-Sciences/dp/0521885884) 45 | > - [Causal Inference:What If](https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2024/01/hernanrobins_WhatIf_2jan24.pdf) 46 | > - [Causal Inference:The Mixtape](https://www.scunning.com/mixtape.html) 47 | 48 | 49 | ## 책 논문 및 자료 모음 50 | 51 | ### 1장-인과추론 소개 52 | | 챕터 | 내용 | 자료 링크 | 53 | | ------ |------|------| 54 | | 1.6 인과효과 식별하기 | 더 알아보기 | [Causal Inference and Data Fusion in Econometrics](https://arxiv.org/abs/1912.09104) | 55 | 56 | 57 | ### 2장-무작위 실험 및 기초 통계 리뷰 58 | | 챕터 | 내용 | 자료 링크 | 59 | | ------ |------|------| 60 | | 2.2 A/B 테스트 사례 | 시뮬레이션 데이터와 실제 데이터 비교 | [A Randomized Assessment of Online Learning](https://www.aeaweb.org/articles?id=10.1257/aer.p20161057) | 61 | | 2.4 가장 위험한 수식 | 하워드 웨이너의 유명한 글 | [The Most Dangerous Equation](https://www.americanscientist.org/article/the-most-dangerous-equation) | 62 | | 2.5 추정값의 표준오차 | MIT의 통계학 입문 강좌 | [Introduction to statistics](https://www.youtube.com/watch?v=VPZD_aij8H0&list=PLUl4u3cNGP60uVBMaoNERc6knT_MgPKS0) | 63 | | 2.6 신뢰구간 | 실제 사례: 코로나19 백신의 효과 | [Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine](https://www.nejm.org/doi/full/10.1056/nejmoa2035389) | 64 | | 2.8 p 값 | 실제 사례: 실제 사례: 대면 강의 vs. 온라인 강의 | [Is It Live or Is It Internet? Experimental Estimates of the Effects of Online Instruction on Student Learning](https://www.journals.uchicago.edu/doi/10.1086/669930) | 65 | | 2.10 표본 크기 계산 | 더 알아보기 | [A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments](https://drive.google.com/file/d/1oK2HpKKXeQLX6gQeQpfEaCGZtNr2kR76/view) | 66 | 67 | 68 | ### 3장-그래프 인과모델 69 | | 챕터 | 내용 | 자료 링크 | 70 | | ------ |------|------| 71 | | 3.7.2 랜덤화 재해석 | 민감도 분석과 부분 식별 | [Making Sense of Sensitivity: Extending Omitted Variable Bias](https://academic.oup.com/jrsssb/article/82/1/39/7056023) | 72 | | 3.8.1 충돌부를 조건부 설정 | 더 알아보기 | [DAGitty](https://www.dagitty.net/), [A Crash Course in Good and Bad Controls](https://journals.sagepub.com/doi/epub/10.1177/00491241221099552) | 73 | 74 | 75 | ### 4장-유용한 선형회귀 76 | | 챕터 | 내용 | 자료 링크 | 77 | | ------ |------|------| 78 | | 4.1 선형회귀의 필요성 | OLS 연구 | [Difference-in-differences with variation in treatment timing](https://www.sciencedirect.com/science/article/abs/pii/S0304407621001445), [Interpreting Ols Estimands When Treatment Effects are Heterogeneous: Smaller Groups](https://direct.mit.edu/rest/article/104/3/501/97692/Interpreting-OLS-Estimands-When-Treatment-Effects), [Contamination Bias in Linear Regressions](https://arxiv.org/abs/2106.05024) | 79 | | 4.7.5 평균 제거와 고정효과 | 패널데이터를 사용한 인과추론 논문 | [On the Pooling of Time Series and Cross Section Data](https://www.jstor.org/stable/1913646) | 80 | | 4.9 중립 통제변수 | 잡음 제거 기법 | [Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data](http://robotics.stanford.edu/~ronnyk/2013-02CUPEDImprovingSensitivityOfControlledExperiments.pdf) | 81 | 82 | 83 | ### 5장-성향점수 84 | | 챕터 | 내용 | 자료 링크 | 85 | | ------ |------|------| 86 | | 5.1 관리자 교육의 효과 | 민감도 분석과 부분 식별 | [Estimating Treatment Effects with Causal Forests: An Application](https://arxiv.org/abs/1902.07409) | 87 | | 5.3.3 성향점수 매칭 | 민감도 분석과 부분 식별 | [Why Propensity Scores Should Not Be Used for Matching](https://gking.harvard.edu/publications/why-Propensity-Scores-Should-Not-Be-Used-Formatching) | 88 | | 5.3.5 역확률 가중치의 분산 | 실제 사례: 인과적 콘텍스트 밴딧 | [A Contextual-Bandit Approach to Personalized News Article Recommendation](https://arxiv.org/abs/1003.0146) | 89 | | 5.5 이중 강건 추정 | 이중 강건 추정량 주석 | [An Introduction to the Augmented Inverse Propensity Weighted Estimator](https://www.law.berkeley.edu/files/AIPW(1).pdf) | 90 | | 5.5.2 결과 모델링이 쉬운 경우 | 더 알아보기 | [Comment: Performance of Double-Robust Estimators When “Inverse Probability” Weights Are Highly Variable](https://arxiv.org/pdf/0804.2965.pdf) | 91 | | 5.6 연속형 처치에서의 일반화 성향점수 | 연속형 처치 | [Causal inference with a continuous treatment and outcome: Alternative estimators for parametric dose-response functions with applications](https://www.proquest.com/openview/888e50198b2563222f8e2030e5a8c9e7/1?pq-origsite=gscholar&cbl=18750) | 92 | 93 | 94 | ### 6장-이질적 처치효과 95 | | 챕터 | 내용 | 자료 링크 | 96 | | ------ |------|------| 97 | | 6.7 누적 이득 곡선 | 더 알아보기 | [Nubank fklearn](https://github.com/nubank/fklearn), [Causal inference and uplift modeling a review of the literature](https://proceedings.mlr.press/v67/gutierrez17a/gutierrez17a.pdf), [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://arxiv.org/abs/2211.01939) | 98 | | 6.8 목표 변환 | 더 알아보기 | [Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference](https://arxiv.org/abs/2007.05188) | 99 | | 6.9.2 이진 결과 | 더 알아보기 | [Causal Classification: Treatment Effect Estimation vs. Outcome Prediction](https://jmlr.org/papers/v23/19-480.html) | 100 | 101 | 102 | ### 7장-메타러너 103 | | 챕터 | 내용 | 자료 링크 | 104 | | ------ |------|------| 105 | | 7.1 이산형 처치 메타러너 | 인과추론 라이브러리 | [EconML 공식문서](https://econml.azurewebsites.net/), [CausalML 공식문서](https://causalml.readthedocs.io/en/latest/about.html) | 106 | | 7.1.1 T 러너 | 더 알아보기 | [Metalearners for estimating heterogeneous treatment effects using machine learning](https://www.pnas.org/doi/full/10.1073/pnas.1804597116) | 107 | | 7.2.1 S 러너 | S 러너의 편향 | [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://www.aeaweb.org/articles?id=10.1257/aer.p20171038) | 108 | | 7.2.2 이중/편향 제거 머신러닝 | 트리 기반 및 신경망 러너 | [Nonparametric Estimation of Heterogeneous Treatment Effects:From Theory to Learning Algorithms](https://proceedings.mlr.press/v130/curth21a/curth21a.pdf), [Learning Representations for Counterfactual Inference](https://proceedings.mlr.press/v48/johansson16.pdf) | 109 | 110 | 111 | ### 8장-이중차분법 112 | | 챕터 | 내용 | 자료 링크 | 113 | | ------ |------|------| 114 | | 8.2 표준 이중차분법 | 실제 사례: 최저임금과 고용 | [Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania](https://davidcard.berkeley.edu/papers/njmin-aer.pdf) | 115 | | 8.2.5 추론 | 더 알아보기 | [When Should You Adjust Standard Errors for Clustering?](https://academic.oup.com/qje/article/138/1/1/6750017), [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1920957) | 116 | | 8.3 식별 가정 | 더 알아보기 | [When Is Parallel Trends Sensitive to Functional Form?](https://cowles.yale.edu/sites/default/files/2022-11/2010.04814.pdf) | 117 | | 8.3.2 비기대 가정과 SUTVA | 공간적 파급 효과 | [Difference-in-Differences Estimation with Spatial Spillovers](https://arxiv.org/abs/2105.03737) | 118 | | 8.3.5 피드백 없음 | 순차적 무시 가능성 | [Causal Inference with Time-Series Cross-Sectional Data: A Reflection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3979613) | 119 | | 8.3.6 이월 효과와 시차종속변수 없음 | 순차적 무시 가능성 | [When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?](https://imai.fas.harvard.edu/research/files/FEmatch.pdf) | 120 | | 8.6.3 최종 결과 | 이중 강건 이중차분법 | [Doubly robust difference-in-differences estimators](https://www.sciencedirect.com/science/article/abs/pii/S0304407620301901) | 121 | | 8.7 처치의 시차 도입 | 이중 강건 이중차분법 | [Difference-in-differences with variation in treatment timing](https://www.sciencedirect.com/science/article/abs/pii/S0304407621001445) | 122 | | 8.7 처치의 시차 도입 | 실제 사례: 개발도상국의 고등교육과 성장 | [Higher Education Expansion, Labor Market, and Firm Productivity in Vietnam](https://sites.google.com/view/khoavu-umn/job-market-paper) | 123 | | 8.7.1 시간에 따른 이질적 효과 | 더 알아보기 | [Estimating dynamic treatment effects in event studies with heterogeneous treatment effects](https://www.sciencedirect.com/science/article/abs/pii/S030440762030378X), [Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3906345), [Difference-in-Differences with multiple time periods](https://www.sciencedirect.com/science/article/abs/pii/S0304407620303948) | 124 | 125 | 126 | ### 9장-통제집단합성법 127 | | 챕터 | 내용 | 자료 링크 | 128 | | ------ |------|------| 129 | | 9.4 표준 통제집단합성법 | 통제집단합성법에 대한 가정 | [Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects](https://www.aeaweb.org/articles?id=10.1257/jel.20191450) | 130 | | 9.7 추론 | 더 알아보기 | [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://arxiv.org/abs/1712.09089) | 131 | | 9.8.4 통제집단합성법과 이중차분법 | 원본 합성 이중차분법 | [Synthetic Difference-in-Differences](https://www.aeaweb.org/articles?id=10.1257/aer.20190159) | 132 | | 9.9 요약 | 일반화 통제집단합성법 | [Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models](https://www.cambridge.org/core/journals/political-analysis/article/generalized-synthetic-control-method-causal-inference-with-interactive-fixed-effects-models/B63A8BD7C239DD4141C67DA10CD0E4F3), [A Bayesian Alternative to Synthetic Control for Comparative Case Studies](https://www.cambridge.org/core/journals/political-analysis/article/bayesian-alternative-to-synthetic-control-for-comparative-case-studies/C23BD67E4BBBB8C88ADAEAE169696A45) | 133 | | 9.9 요약 | 실제 사례: causalimpact 라이브러리 | [CausalImpact Github](https://github.com/google/CausalImpact?tab=readme-ov-file) | 134 | 135 | 136 | ### 10장-스위치백 실험 137 | | 챕터 | 내용 | 자료 링크 | 138 | | ------ |------|------| 139 | | 10.2.2 무작위 탐색 | 최적화 | [Synthetic Controls for Experimental Design](https://economics.mit.edu/sites/default/files/2023-02/synthetic%20experiments.pdf), [Designing Experiments with Synthetic Controls](https://mackinstitute.wharton.upenn.edu/wp-content/uploads/2020/03/Wernerfelt-Nils-Doudchenko-Nick-Gilinson-David-and-Taylor-Sean_Designing-Experiments-with-Synthetic-Controls.pdf) | 140 | | 10.2.2 무작위 탐색 | 다른 실험 목표 | [Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls](https://proceedings.neurips.cc/paper/2021/hash/48d23e87eb98cc2227b5a8c33fa00680-Abstract.html) | 141 | | 10.3.5 강건한 분산 | 더 적은 가정으로 m 찾기 | [Design and Analysis of Switchback Experiments](https://arxiv.org/abs/2009.00148) | 142 | 143 | 144 | ### 11장-도구변수 145 | | 챕터 | 내용 | 자료 링크 | 146 | | ------ |------|------| 147 | | 11.9.4 밀도 불연속 테스트 | 최적화 | [Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test](https://eml.berkeley.edu/~jmccrary/mccrary2006_DCdensity.pdf) | 148 | | 11.10 요약 | 최적화 | [Does Compulsory School Attendance Affect Schooling and Earnings?](https://www.jstor.org/stable/2937954) | 149 | 150 | 151 | ### 12장-더 배울 내용 152 | | 챕터 | 내용 | 자료 링크 | 153 | | ------ |------|------| 154 | | 12.1 인과관계 발견 | 인과관계 발견 | [Causal Discovery Toolbox: Uncover causal relationships in Python](https://arxiv.org/abs/1903.02278) | 155 | | 12.3 인과적 강화학습 | 콘텍스트 밴딧 | [Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning](https://arxiv.org/abs/2211.12004) | 156 | | 12.3 인과적 강화학습 | 미국 경제학회 웹캐스트 | [2022 Continuing Education Webcasts](https://www.aeaweb.org/conference/cont-ed/2022-webcasts) | 157 | | 12.4 인과 예측 | 미국 경제학회 웹캐스트 | [2019 AEA Continuing Education Webcasts](https://www.aeaweb.org/conference/cont-ed/2019-webcasts) | 158 | | 12.5 도메인 적응 | Concept Drift | [Learning under Concept Drift: A Review](https://ieeexplore.ieee.org/document/8496795) | -------------------------------------------------------------------------------- /시뮬레이션 데이터.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chapter 1" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": { 14 | "ExecuteTime": { 15 | "end_time": "2023-01-25T09:11:17.321955Z", 16 | "start_time": "2023-01-25T09:11:17.289189Z" 17 | } 18 | }, 19 | "outputs": [], 20 | "source": [ 21 | "import pandas as pd\n", 22 | "import numpy as np\n", 23 | "\n", 24 | "units = range(1, 500+1)\n", 25 | "\n", 26 | "np.random.seed(1)\n", 27 | "\n", 28 | "data = pd.DataFrame(dict(\n", 29 | " store = np.repeat(units, 4),\n", 30 | " unit_fe = np.repeat(np.random.normal(0, 5, size=len(units)), 4),\n", 31 | " weeks_to_xmas = np.tile([3,2,1,0], len(units)),\n", 32 | " avg_week_sales = np.repeat(np.random.gamma(20, 1, size=len(units)), 4).round(2),\n", 33 | ")\n", 34 | ").assign(\n", 35 | " is_on_sale = lambda d: (\n", 36 | " (d[\"unit_fe\"] > 0)*np.random.binomial(1, 0.2, d.shape[0])\n", 37 | " | (d[\"avg_week_sales\"] > np.random.gamma(25, 1, size=len(units)*4))\n", 38 | " | (np.random.normal(d[\"weeks_to_xmas\"], 2) > 2)*np.random.binomial(1, 0.7, size=d.shape[0]) # xmas\n", 39 | " )*1,\n", 40 | ").assign(\n", 41 | " y0 = lambda d: (-10 + 10*d[\"unit_fe\"] + d[\"avg_week_sales\"] \n", 42 | " + 2*d[\"avg_week_sales\"]*d[\"weeks_to_xmas\"])\n", 43 | ").assign(\n", 44 | " y1 = lambda d: d[\"y0\"] + 50\n", 45 | ").assign(\n", 46 | " tau = lambda d: d[\"y1\"] - d[\"y0\"],\n", 47 | " weekly_amount_sold = lambda d: np.random.normal(np.where(d[\"is_on_sale\"] == 1, d[\"y1\"], d[\"y0\"]), 10).clip(0, np.inf).round(2)\n", 48 | ").drop(columns=[\"unit_fe\", \"y0\", \"y1\", \"tau\"])\n", 49 | "\n", 50 | "data.to_csv(\"./causal-inference-in-python/data/xmas_sales.csv\", index=False)\n" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 1, 56 | "metadata": { 57 | "ExecuteTime": { 58 | "end_time": "2023-01-24T19:58:50.923765Z", 59 | "start_time": "2023-01-24T19:58:50.362564Z" 60 | } 61 | }, 62 | "outputs": [], 63 | "source": [ 64 | "import pandas as pd\n", 65 | "import numpy as np\n", 66 | "pd.set_option('display.max_rows', 5)\n", 67 | "\n", 68 | "np.random.seed(123)\n", 69 | "data = (\n", 70 | " pd.read_csv(\"./causal-inference-in-python/data/online_classroom.csv\")\n", 71 | " .assign(cross_sell_email = lambda d: np.select(\n", 72 | " [d[\"format_ol\"].astype(bool), d[\"format_blended\"].astype(bool)],\n", 73 | " [\"no_email\", \"long\"],\n", 74 | " default=\"short\")\n", 75 | " )\n", 76 | " .assign(age = lambda d: np.random.gamma(1.5, 5, d.shape[0]).astype(int)+14)\n", 77 | " .assign(conversion = lambda d: ((d[\"falsexam\"]-d[\"age\"]) > 70).astype(int))\n", 78 | " .drop(columns=[\"asian\", \"black\", \"hawaiian\", \"hispanic\", \"unknown\", \"white\", \"format_ol\", \"format_blended\", \"falsexam\"])\n", 79 | ")\n", 80 | "\n", 81 | "data.to_csv(\"./causal-inference-in-python/data/cross_sell_email.csv\", index=False)" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "# Chapter 4" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 2, 94 | "metadata": { 95 | "ExecuteTime": { 96 | "end_time": "2023-01-24T19:58:50.937159Z", 97 | "start_time": "2023-01-24T19:58:50.925222Z" 98 | } 99 | }, 100 | "outputs": [], 101 | "source": [ 102 | "np.random.seed(123)\n", 103 | "data = (\n", 104 | " pd.read_csv(\"./causal-inference-in-python/data/online_classroom.csv\")\n", 105 | " .assign(recommender = lambda d: np.select(\n", 106 | " [d[\"format_ol\"].astype(bool), d[\"format_blended\"].astype(bool)],\n", 107 | " [\"benchmark\", \"benchmark\"],\n", 108 | " default=\"challenger\"))\n", 109 | " .assign(age = lambda d: np.random.gamma(1.5, 5, d.shape[0]).astype(int)+14)\n", 110 | " .assign(tenure = lambda d: np.random.gamma(1, 1.1, d.shape[0]).astype(int).clip(0, 4))\n", 111 | " .assign(watch_time = lambda d: np.random.normal(3*(d[\"recommender\"]==\"challenger\") + 10*d[\"tenure\"] - 0.8*d[\"age\"] + 0.001*d[\"age\"]**2, 11))\n", 112 | " .assign(watch_time = lambda d: ((d[\"watch_time\"]- d[\"watch_time\"].min())/(d[\"watch_time\"].max()+20 - d[\"watch_time\"].min())*6).round(2))\n", 113 | " .drop(columns=[\"asian\", \"gender\", \"black\", \"hawaiian\", \"hispanic\", \"unknown\", \"white\", \"format_ol\", \"format_blended\", \"falsexam\"])\n", 114 | ")\n", 115 | "\n", 116 | "data.to_csv(\"./causal-inference-in-python/data/rec_ab_test.csv\", index=False)" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 3, 122 | "metadata": { 123 | "ExecuteTime": { 124 | "end_time": "2023-01-24T19:58:51.452368Z", 125 | "start_time": "2023-01-24T19:58:50.940799Z" 126 | } 127 | }, 128 | "outputs": [], 129 | "source": [ 130 | "np.random.seed(123)\n", 131 | "risk_data = (\n", 132 | " pd.read_csv(\"./causal-inference-in-python/data/wage.csv\")\n", 133 | " .sample(50000, replace=True)\n", 134 | " .assign(wage = lambda d: np.random.normal(100 + d[\"wage\"], 50).round(-1))\n", 135 | " .assign(credit_score1 = lambda d: np.random.normal(100 + 0.1*d[\"wage\"] + d[\"urban\"] + d[\"educ\"] + 2*d[\"exper\"], 50).round(-1))\n", 136 | " .assign(credit_score2 = lambda d: np.random.normal(100 + 0.05*d[\"wage\"] + d[\"hours\"] + d[\"age\"] + 2*d[\"IQ\"], 50).round(-1))\n", 137 | " \n", 138 | " .assign(credit_score1 = lambda d: np.round(1000*(d[\"credit_score1\"] - d[\"credit_score1\"].min() + 20)/(d[\"credit_score1\"].max() - d[\"credit_score1\"].min()), 0))\n", 139 | " .assign(credit_score2 = lambda d: np.round(1000*(d[\"credit_score2\"] - d[\"credit_score2\"].min() + 20)/(d[\"credit_score2\"].max() - d[\"credit_score2\"].min()), 0))\n", 140 | "\n", 141 | " \n", 142 | " .assign(credit_limit = lambda d: np.random.normal(500 \n", 143 | " + 1.5*pd.IntervalIndex(pd.qcut(d[\"credit_score1\"], 10)).mid\n", 144 | " + 1.5*pd.IntervalIndex(pd.qcut(d[\"wage\"], 10)).mid,\n", 145 | " 1000).round(-2).clip(200, np.inf))\n", 146 | " .assign(default = lambda d: (np.random.normal(+0\n", 147 | " - 0.1*d[\"credit_score1\"] \n", 148 | " - 0.5*d[\"credit_score2\"] \n", 149 | " + 0.05*d[\"IQ\"] \n", 150 | " + 0.005*d[\"credit_limit\"] \n", 151 | " -0.17*d[\"wage\"] \n", 152 | " \n", 153 | " , 375) > -50\n", 154 | " ).astype(int))\n", 155 | " .drop(columns=[\"IQ\", \"lhwage\", \"tenure\", \"black\", \"hours\", \"sibs\", \"south\", \"urban\", \"brthord\", \"meduc\", \"feduc\", \"age\"])\n", 156 | " .reset_index(drop=True)\n", 157 | ")\n", 158 | "\n", 159 | "spend_data = (risk_data\n", 160 | " .assign(spend = lambda d: (np.random.normal(50 \n", 161 | " + 120*np.power(d[\"credit_limit\"], 1/2.5)\n", 162 | " - d[\"credit_score1\"] \n", 163 | " + 1.2 * d[\"wage\"]\n", 164 | " - 10*d[\"exper\"]\n", 165 | " - 10*d[\"educ\"]\n", 166 | " + 400*d[\"married\"]\n", 167 | " ).astype(int)))\n", 168 | " .drop(columns=[\"default\"])\n", 169 | " )\n", 170 | "\n", 171 | "risk_data.to_csv(\"./causal-inference-in-python/data/risk_data.csv\", index=False)\n", 172 | "spend_data.to_csv(\"./causal-inference-in-python/data/spend_data.csv\", index=False)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 4, 178 | "metadata": { 179 | "ExecuteTime": { 180 | "end_time": "2023-01-24T19:58:51.509074Z", 181 | "start_time": "2023-01-24T19:58:51.453916Z" 182 | } 183 | }, 184 | "outputs": [], 185 | "source": [ 186 | "np.random.seed(123)\n", 187 | "risk_data_rnd = (\n", 188 | " pd.read_csv(\"./causal-inference-in-python/data/wage.csv\")\n", 189 | " .sample(10000, replace=True)\n", 190 | " .assign(wage = lambda d: np.random.normal(100 + d[\"wage\"], 50).round(-1))\n", 191 | " .assign(credit_score1 = lambda d: np.random.normal(100 + 0.1*d[\"wage\"] + d[\"urban\"] + d[\"educ\"] + 2*d[\"exper\"], 50).round(-1))\n", 192 | " .assign(credit_score2 = lambda d: np.random.normal(100 + 0.05*d[\"wage\"] + d[\"hours\"] + d[\"age\"] + 2*d[\"IQ\"], 50).round(-1))\n", 193 | " \n", 194 | " .assign(credit_score1 = lambda d: np.round(1000*(d[\"credit_score1\"] - d[\"credit_score1\"].min() + 20)/(d[\"credit_score1\"].max() - d[\"credit_score1\"].min()), 0))\n", 195 | " .assign(credit_score2 = lambda d: np.round(1000*(d[\"credit_score2\"] - d[\"credit_score2\"].min() + 20)/(d[\"credit_score2\"].max() - d[\"credit_score2\"].min()), 0))\n", 196 | " \n", 197 | " .assign(credit_score1_buckets = lambda d: ((d[\"credit_score1\"] / 200).round(0) * 200).astype(int))\n", 198 | " \n", 199 | " .assign(credit_limit = lambda d: (np.random.beta(1 + d[\"credit_score1_buckets\"]/100, 6)*10000).round(-2))\n", 200 | " .assign(default = lambda d: (np.random.normal(+0\n", 201 | " - 0.1*d[\"credit_score1\"] \n", 202 | " - 0.5*d[\"credit_score2\"] \n", 203 | " + 0.05*d[\"IQ\"] \n", 204 | " + 0.5*d[\"sibs\"]\n", 205 | " - 0.5*d[\"feduc\"]\n", 206 | " + 0.005*d[\"credit_limit\"] \n", 207 | " -0.17*d[\"wage\"] \n", 208 | " \n", 209 | " , 100) > -300\n", 210 | " ).astype(int))\n", 211 | " .drop(columns=[\"IQ\", \"lhwage\", \"tenure\", \"black\", \"hours\", \"sibs\", \"south\", \"urban\", \"brthord\", \"meduc\", \"feduc\", \"age\"])\n", 212 | " .reset_index(drop=True)\n", 213 | ")\n", 214 | "\n", 215 | "risk_data_rnd.to_csv(\"./causal-inference-in-python/data/risk_data_rnd.csv\", index=False)" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": 4, 221 | "metadata": { 222 | "ExecuteTime": { 223 | "end_time": "2023-01-25T09:16:08.938041Z", 224 | "start_time": "2023-01-25T09:16:08.903066Z" 225 | } 226 | }, 227 | "outputs": [], 228 | "source": [ 229 | "np.random.seed(123)\n", 230 | "spend_data_rnd = (\n", 231 | " pd.read_csv(\"./causal-inference-in-python/data/wage.csv\")\n", 232 | " .sample(500, replace=True)\n", 233 | " .assign(wage = lambda d: np.random.normal(100 + d[\"wage\"], 50).round(-1))\n", 234 | " .assign(credit_score1 = lambda d: np.random.normal(100, 50, len(d)).round(-1))\n", 235 | " \n", 236 | " .assign(credit_score1 = lambda d: np.round(1000*(d[\"credit_score1\"] - d[\"credit_score1\"].min() + 20)/(d[\"credit_score1\"].max() - d[\"credit_score1\"].min()), 0))\n", 237 | " .assign(credit_score1_buckets = lambda d: ((d[\"credit_score1\"] / 200).round(0) * 200).astype(int))\n", 238 | " \n", 239 | " .assign(credit_limit = lambda d: (np.random.beta(1 + d[\"credit_score1_buckets\"]/100, 6)*10000).round(-2))\n", 240 | " .assign(spend = lambda d: (np.random.normal(500 \n", 241 | " + 20*np.power(d[\"credit_limit\"], 1/2)\n", 242 | " + 1.2 * d[\"wage\"]\n", 243 | " - 10*d[\"exper\"]\n", 244 | " - 10*d[\"educ\"]\n", 245 | " + 400*d[\"married\"], 600).astype(int)))\n", 246 | " .drop(columns=[\"IQ\", \"lhwage\", \"tenure\", \"black\", \"hours\", \"sibs\", \"south\", \"urban\", \"brthord\", \"meduc\", \"feduc\", \"age\"])\n", 247 | " .reset_index(drop=True)\n", 248 | ")\n", 249 | "\n", 250 | "spend_data_rnd.to_csv(\"./causal-inference-in-python/data/spend_data_rnd.csv\", index=False)" 251 | ] 252 | }, 253 | { 254 | "cell_type": "markdown", 255 | "metadata": {}, 256 | "source": [ 257 | "# Chapter 5" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": 1, 263 | "metadata": { 264 | "ExecuteTime": { 265 | "end_time": "2023-03-12T18:04:52.239145Z", 266 | "start_time": "2023-03-12T18:04:49.773194Z" 267 | } 268 | }, 269 | "outputs": [], 270 | "source": [ 271 | "import numpy as np\n", 272 | "import pandas as pd\n", 273 | "\n", 274 | "np.random.seed(123)\n", 275 | "\n", 276 | "df = (pd.read_csv(\"https://raw.githubusercontent.com/matheusfacure/python-causality-handbook/master/causal-inference-for-the-brave-and-true/data/learning_mindset.csv\")\n", 277 | " .rename(columns={\"schoolid\": \"departament_id\", \"achievement_score\": \"engagement_score\", \"success_expect\": \"tenure\",\n", 278 | " \"school_achievement\": \"last_engagement_score\", \"school_urbanicity\": \"role\", \"school_poverty\": \"department_score\",\n", 279 | " \"school_size\": \"department_size\", \"ethnicity\": \"n_of_reports\"})\n", 280 | " # reduce overlapp for better examples\n", 281 | " .assign(intervention = lambda d: (d[\"intervention\"].astype(bool) | (np.random.normal(d[\"last_engagement_score\"]) > 1.65)).astype(int))\n", 282 | " .assign(intervention = lambda d: (d[\"intervention\"].astype(bool) | (np.random.normal(d[\"tenure\"], 2) > 7)).astype(int))\n", 283 | " .assign(n_of_reports = lambda d: d[\"n_of_reports\"].clip(0, 8))\n", 284 | " .assign(department_size = lambda d: d.groupby(\"departament_id\")[\"n_of_reports\"].transform(sum))\n", 285 | " .assign(last_engagement_score = lambda d: np.random.normal(d[\"last_engagement_score\"]))\n", 286 | " .drop(columns=[\"frst_in_family\", \"school_mindset\", \"school_ethnic_minority\"]))\n", 287 | "\n", 288 | "\n", 289 | "df.to_csv(\"./causal-inference-in-python/data/management_training.csv\", index=False)" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 2, 295 | "metadata": { 296 | "ExecuteTime": { 297 | "end_time": "2023-03-12T18:04:52.296332Z", 298 | "start_time": "2023-03-12T18:04:52.248495Z" 299 | } 300 | }, 301 | "outputs": [], 302 | "source": [ 303 | "np.random.seed(123)\n", 304 | "\n", 305 | "n = 10000\n", 306 | "\n", 307 | "x1 = np.random.uniform(-1, 1, n)\n", 308 | "x2 = np.random.uniform(-1, 1, n)\n", 309 | "t = np.random.normal(7.5 - 1*(x1+x2), 2).clip(1, 14).round(1)\n", 310 | "y = np.random.normal((23 - 0.8*t - 8*(x1 + x2))).clip(1, np.inf).round(0)\n", 311 | "\n", 312 | "df_cont_t = pd.DataFrame(dict(ml_1=x1, ml_2=x2, interest=t, duration=y))\n", 313 | "\n", 314 | "df_cont_t.to_csv(\"./causal-inference-in-python/data/interest_rate.csv\", index=False)" 315 | ] 316 | }, 317 | { 318 | "cell_type": "markdown", 319 | "metadata": {}, 320 | "source": [ 321 | "# Chapter 6" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 3, 327 | "metadata": { 328 | "ExecuteTime": { 329 | "end_time": "2023-06-26T13:24:24.810974Z", 330 | "start_time": "2023-06-26T13:24:24.700439Z" 331 | } 332 | }, 333 | "outputs": [], 334 | "source": [ 335 | "import pandas as pd\n", 336 | "from pandas.tseries import holiday\n", 337 | "\n", 338 | "np.random.seed(123)\n", 339 | "\n", 340 | "\n", 341 | "month_fe = {1: 23,\n", 342 | " 2: 11,\n", 343 | " 3: 10,\n", 344 | " 4: 9,\n", 345 | " 5: 4,\n", 346 | " 6: 2,\n", 347 | " 7: 10,\n", 348 | " 8: 5,\n", 349 | " 9: 10,\n", 350 | " 10: 20,\n", 351 | " 11: 25,\n", 352 | " 12: 30}\n", 353 | "\n", 354 | "week_fe = {0: 2,\n", 355 | " 1: 2,\n", 356 | " 2: 3,\n", 357 | " 3: 7,\n", 358 | " 4: 15,\n", 359 | " 5: 12,\n", 360 | " 6: 5}\n", 361 | "\n", 362 | "store_fe = {0: 20,\n", 363 | " 1: 14,\n", 364 | " 2: 3,\n", 365 | " 3: 10,\n", 366 | " 4: 9,\n", 367 | " 5: 12,\n", 368 | " 6: 5}\n", 369 | "\n", 370 | "day = pd.Series(pd.date_range(\"2016-01-01\", \"2019-01-01\"))\n", 371 | "weekend = day.dt.weekday >= 5\n", 372 | "is_holiday = day.isin(holiday.USFederalHolidayCalendar().holidays(\"2016-01-01\", \"2019-01-01\"))\n", 373 | "is_dec = day.dt.month == 12\n", 374 | "is_nov = day.dt.month == 11\n", 375 | "\n", 376 | "time_data = pd.DataFrame(dict(\n", 377 | " day=day,\n", 378 | " month = day.dt.month,\n", 379 | " weekday = day.dt.weekday,\n", 380 | " weekend=weekend,\n", 381 | " is_holiday = is_holiday,\n", 382 | " is_dec = day.dt.month == 12,\n", 383 | " is_nov = day.dt.month == 11,\n", 384 | ")).assign(\n", 385 | " month_fe = lambda d: d[\"month\"].map(month_fe),\n", 386 | " week_fe = lambda d: d[\"weekday\"].map(week_fe),\n", 387 | ")\n", 388 | "\n", 389 | "data=(pd.concat([time_data.assign(rest_id=i) for i in range(len(store_fe))])\n", 390 | " .assign(store_fe = lambda d: d[\"rest_id\"].map(store_fe))\n", 391 | " .assign(competitors_price = lambda d: (np.random.beta(5+d[\"is_holiday\"]+d[\"is_dec\"]*2+d[\"is_nov\"], 0.5*d[\"store_fe\"], len(d))*9 + 1).round(2))\n", 392 | " .assign(sales0 = lambda d: np.random.poisson(d[\"month_fe\"] + d[\"store_fe\"] + d[\"week_fe\"]\n", 393 | " + 3*d[\"competitors_price\"] \n", 394 | " + 10*d[\"is_holiday\"] \n", 395 | " + 5*d[\"weekend\"]))\n", 396 | " .assign(effect = lambda d: np.random.poisson(150+d[\"month_fe\"] - d[\"store_fe\"] + d[\"week_fe\"]\n", 397 | " - 40*np.sqrt(d[\"competitors_price\"])\n", 398 | " + 5*d[\"is_holiday\"] \n", 399 | " - 3*d[\"weekend\"]))\n", 400 | " .assign(discounts = lambda d: ((np.random.beta(1, 3, size=len(d)) * 50)/5).astype(int)*5)\n", 401 | " .assign(sales = lambda d: d[\"sales0\"] + 0.5*d[\"effect\"]*d[\"discounts\"])\n", 402 | " [[\"rest_id\", \"day\", \"month\", \"weekday\", \"weekend\", \"is_holiday\", \"is_dec\", \"is_nov\", \"competitors_price\",\n", 403 | " \"discounts\", \"sales\"]])\n", 404 | "\n", 405 | "\n", 406 | "data.to_csv(\"./causal-inference-in-python/data/daily_restaurant_sales.csv\", index=False)" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "# Chapter 7" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 4, 419 | "metadata": { 420 | "ExecuteTime": { 421 | "end_time": "2023-06-26T13:24:28.018989Z", 422 | "start_time": "2023-06-26T13:24:27.911853Z" 423 | } 424 | }, 425 | "outputs": [], 426 | "source": [ 427 | "np.random.seed(1)\n", 428 | "\n", 429 | "categs = {\"vehicle\": 0.1,\n", 430 | " \"food\": 1,\n", 431 | " \"beverage\": 1,\n", 432 | " \"art\": 1,\n", 433 | " \"baby\": 1,\n", 434 | " \"personal_care\": 1,\n", 435 | " \"toys\": 1,\n", 436 | " \"clothing\": 2,\n", 437 | " \"decor\": 1,\n", 438 | " \"cell_phones\": 3,\n", 439 | " \"construction\": 1,\n", 440 | " \"home_appliances\": 1,\n", 441 | " \"electronics\": 2,\n", 442 | " \"sports\": 1,\n", 443 | " \"tools\": 1,\n", 444 | " \"games\": 2,\n", 445 | " \"industry\": 1,\n", 446 | " \"pc\": 2,\n", 447 | " \"jewel\": 1,\n", 448 | " \"books\": 1,\n", 449 | " \"music_books_movies\": 1,\n", 450 | " \"health\": 2,\n", 451 | " }\n", 452 | "\n", 453 | "\n", 454 | "n=10000\n", 455 | "\n", 456 | "age = (18+np.random.beta(2, 7, n)*90).round(0)\n", 457 | "tenure = np.random.exponential(0.5, n).round(0)\n", 458 | "ammount_spent = (np.random.exponential(1, n)*100 + np.random.binomial(1, 0.01, n)*np.random.uniform(500, 50000, n)).round(2)\n", 459 | "\n", 460 | "categ_purchage = {cat: np.random.poisson(l, n) for cat, l in categs.items()}\n", 461 | "\n", 462 | "X = pd.DataFrame(categ_purchage).assign(\n", 463 | " age=age,\n", 464 | " tenure=tenure,\n", 465 | " ammount_spent=ammount_spent,\n", 466 | ")\n", 467 | "\n", 468 | "mkt_email_rnd = np.random.binomial(1, 0.5, n) \n", 469 | "\n", 470 | "coefs = np.concatenate([\n", 471 | " np.random.uniform(-1, 1, len(categs)),\n", 472 | " np.array([1, 20, 0.4])\n", 473 | "])\n", 474 | "y0 = np.random.exponential(X.values.dot(coefs)) \n", 475 | "\n", 476 | "cate_coef = np.concatenate([\n", 477 | " np.random.uniform(0, 100, len(categs)),\n", 478 | " np.array([-1, -20, 0])\n", 479 | "])\n", 480 | "\n", 481 | "tau = np.random.normal(X.values.dot(cate_coef))\n", 482 | "\n", 483 | "data_rnd = X.assign(\n", 484 | " mkt_email = mkt_email_rnd,\n", 485 | " next_mnth_pv = y0 + tau*mkt_email_rnd\n", 486 | ")[[\"mkt_email\", \"next_mnth_pv\", \"age\", \"tenure\", \"ammount_spent\"] + list(categs.keys())].round(2)\n", 487 | "\n", 488 | "\n", 489 | "data_rnd.to_csv(\"./causal-inference-in-python/data/email_rnd_data.csv\", index=False)" 490 | ] 491 | }, 492 | { 493 | "cell_type": "code", 494 | "execution_count": 5, 495 | "metadata": { 496 | "ExecuteTime": { 497 | "end_time": "2023-06-26T13:24:30.494095Z", 498 | "start_time": "2023-06-26T13:24:28.482269Z" 499 | } 500 | }, 501 | "outputs": [], 502 | "source": [ 503 | "np.random.seed(2)\n", 504 | "\n", 505 | "n=300000\n", 506 | "\n", 507 | "age = (18+np.random.beta(2, 7, n)*90).round(0)\n", 508 | "tenure = np.random.exponential(0.5, n).round(0)\n", 509 | "ammount_spent = (np.random.exponential(1, n)*100 + np.random.binomial(1, 0.01, n)*np.random.uniform(500, 50000, n)).round(2)\n", 510 | "\n", 511 | "categ_purchage = {cat: np.random.poisson(l, n) for cat, l in categs.items()}\n", 512 | "\n", 513 | "X = pd.DataFrame(categ_purchage).assign(\n", 514 | " age=age,\n", 515 | " tenure=tenure,\n", 516 | " ammount_spent=ammount_spent,\n", 517 | ")\n", 518 | "\n", 519 | "coefs_ps = np.concatenate([\n", 520 | " np.random.uniform(-1, 1, len(categs)),\n", 521 | " np.array([-1, 40, 0.4])\n", 522 | "])\n", 523 | "\n", 524 | "\n", 525 | "mkt_email_biased = (np.random.normal(X.values.dot(coefs_ps), 2000) > 0).astype(int)\n", 526 | "\n", 527 | "y0 = np.random.exponential(X.values.dot(coefs)) \n", 528 | "tau = np.random.normal(X.values.dot(cate_coef))\n", 529 | "\n", 530 | "data_biased = X.assign(\n", 531 | " mkt_email = mkt_email_biased,\n", 532 | " next_mnth_pv = y0 + tau*mkt_email_biased\n", 533 | ")[[\"mkt_email\", \"next_mnth_pv\", \"age\", \"tenure\", \"ammount_spent\"] + list(categs.keys())].round(2)\n", 534 | "\n", 535 | "data_biased.to_csv(\"./causal-inference-in-python/data/email_obs_data.csv\", index=False)" 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": 6, 541 | "metadata": { 542 | "ExecuteTime": { 543 | "end_time": "2023-06-26T13:24:33.155603Z", 544 | "start_time": "2023-06-26T13:24:31.504219Z" 545 | } 546 | }, 547 | "outputs": [], 548 | "source": [ 549 | "import pandas as pd\n", 550 | "import numpy as np\n", 551 | "from matplotlib import pyplot as plt\n", 552 | "import seaborn as sns\n", 553 | "\n", 554 | "from pandas.tseries import holiday\n", 555 | "\n", 556 | "np.random.seed(123)\n", 557 | "\n", 558 | "\n", 559 | "month_fe = {1: 23,\n", 560 | " 2: 11,\n", 561 | " 3: 10,\n", 562 | " 4: 9,\n", 563 | " 5: 4,\n", 564 | " 6: 2,\n", 565 | " 7: 10,\n", 566 | " 8: 5,\n", 567 | " 9: 10,\n", 568 | " 10: 20,\n", 569 | " 11: 25,\n", 570 | " 12: 30}\n", 571 | "\n", 572 | "week_fe = {0: 2,\n", 573 | " 1: 2,\n", 574 | " 2: 3,\n", 575 | " 3: 7,\n", 576 | " 4: 15,\n", 577 | " 5: 12,\n", 578 | " 6: 5}\n", 579 | "\n", 580 | "store_fe = {0: 20,\n", 581 | " 1: 14,\n", 582 | " 2: 3,\n", 583 | " 3: 10,\n", 584 | " 4: 9,\n", 585 | " 5: 12,\n", 586 | " 6: 5}\n", 587 | "\n", 588 | "day = pd.Series(pd.date_range(\"2016-01-01\", \"2019-01-01\"))\n", 589 | "weekend = day.dt.weekday >= 5\n", 590 | "is_holiday = day.isin(holiday.USFederalHolidayCalendar().holidays(\"2016-01-01\", \"2019-01-01\"))\n", 591 | "is_dec = day.dt.month == 12\n", 592 | "is_nov = day.dt.month == 11\n", 593 | "\n", 594 | "time_data = pd.DataFrame(dict(\n", 595 | " day=day,\n", 596 | " month = day.dt.month,\n", 597 | " weekday = day.dt.weekday,\n", 598 | " weekend=weekend,\n", 599 | " is_holiday = is_holiday,\n", 600 | " is_dec = day.dt.month == 12,\n", 601 | " is_nov = day.dt.month == 11,\n", 602 | ")).assign(\n", 603 | " month_fe = lambda d: d[\"month\"].map(month_fe),\n", 604 | " week_fe = lambda d: d[\"weekday\"].map(week_fe),\n", 605 | ")\n", 606 | "\n", 607 | "data_cont=(pd.concat([time_data.assign(rest_id=i) for i in range(len(store_fe))])\n", 608 | " .assign(store_fe = lambda d: d[\"rest_id\"].map(store_fe))\n", 609 | " .assign(competitors_price = lambda d: (np.random.beta(5+d[\"is_holiday\"]+d[\"is_dec\"]*2+d[\"is_nov\"], 0.5*d[\"store_fe\"], len(d))*9 + 1).round(2))\n", 610 | " .assign(sales0 = lambda d: np.random.poisson(d[\"month_fe\"] + d[\"store_fe\"] + d[\"week_fe\"]\n", 611 | " + 3*d[\"competitors_price\"] \n", 612 | " + 10*d[\"is_holiday\"] \n", 613 | " + 5*d[\"weekend\"]))\n", 614 | " .assign(effect = lambda d: np.random.poisson(150+d[\"month_fe\"] - d[\"store_fe\"] + d[\"week_fe\"]\n", 615 | " - 40*np.sqrt(d[\"competitors_price\"])\n", 616 | " + 5*d[\"is_holiday\"] \n", 617 | " - 3*d[\"weekend\"]))\n", 618 | " .assign(discounts = lambda d: ((np.random.beta(1, 3, size=len(d)) * 50)/5).astype(int)*5)\n", 619 | " .assign(sales = lambda d: d[\"sales0\"] + 0.5*d[\"effect\"]*d[\"discounts\"])\n", 620 | " [[\"rest_id\", \"day\", \"month\", \"weekday\", \"weekend\", \"is_holiday\", \"is_dec\", \"is_nov\", \"competitors_price\",\n", 621 | " \"discounts\", \"sales\"]])\n", 622 | "\n", 623 | "\n", 624 | "data_cont.to_csv(\"./causal-inference-in-python/data/discount_data.csv\", index=False)" 625 | ] 626 | }, 627 | { 628 | "cell_type": "markdown", 629 | "metadata": {}, 630 | "source": [ 631 | "# Chapter 8" 632 | ] 633 | }, 634 | { 635 | "cell_type": "code", 636 | "execution_count": 7, 637 | "metadata": { 638 | "ExecuteTime": { 639 | "end_time": "2023-06-26T13:24:36.557346Z", 640 | "start_time": "2023-06-26T13:24:36.438111Z" 641 | } 642 | }, 643 | "outputs": [], 644 | "source": [ 645 | "date = pd.date_range(\"2021-05-01\", \"2021-07-31\", freq=\"D\")\n", 646 | "cohorts = pd.to_datetime([\"2021-05-15\", \"2021-06-04\", \"2021-06-20\"]).date\n", 647 | "poss_regions = [\"S\", \"N\", \"W\", \"E\"]\n", 648 | "\n", 649 | "reg_ps = dict(zip(poss_regions, [.3, .6, .7, .8]))\n", 650 | "reg_fe = dict(zip(poss_regions, [20, 16, 8, 2]))\n", 651 | "reg_trend = dict(zip(poss_regions, [0, 0.2, .4, .6]))\n", 652 | "\n", 653 | "units = np.array(range(1, 200+1))\n", 654 | "\n", 655 | "np.random.seed(123)\n", 656 | "\n", 657 | "unit_reg = np.random.choice(poss_regions, len(units))\n", 658 | "exp_trend = np.random.exponential(0.01, len(units))\n", 659 | "treated_unit = np.random.binomial(1, np.vectorize(reg_ps.__getitem__)(unit_reg))\n", 660 | "\n", 661 | "# staggered addopton dgp\n", 662 | "df = pd.DataFrame(dict(\n", 663 | " date = np.tile(date.date, len(units)),\n", 664 | " city = np.repeat(units, len(date)),\n", 665 | " region = np.repeat(unit_reg, len(date)),\n", 666 | " treated_unit = np.repeat(treated_unit, len(date)),\n", 667 | " cohort = np.repeat(np.random.choice(cohorts, len(units)), len(date)),\n", 668 | " eff_heter = np.repeat(np.random.exponential(1, size=len(units)), len(date)),\n", 669 | " \n", 670 | " unit_fe = np.repeat(np.random.normal(0, 2, size=len(units)), len(date)),\n", 671 | " time_fe = np.tile(np.random.normal(size=len(date)), len(units)),\n", 672 | " week_day = np.tile(date.weekday, len(units)),\n", 673 | " w_seas = np.tile(abs(5-date.weekday) % 7, len(units)),\n", 674 | ")).assign(\n", 675 | " reg_fe = lambda d: d[\"region\"].map(reg_fe), \n", 676 | " reg_trend = lambda d: d[\"region\"].map(reg_trend), \n", 677 | " reg_ps = lambda d: d[\"region\"].map(reg_ps), \n", 678 | " trend = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days,\n", 679 | " day = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days,\n", 680 | " cohort = lambda d: np.where(d[\"treated_unit\"] == 1, d[\"cohort\"], pd.to_datetime(\"2100-01-01\")),\n", 681 | ").assign(\n", 682 | " treated = lambda d: ((d[\"date\"] >= d[\"cohort\"]) & d[\"treated_unit\"] == 1).astype(int),\n", 683 | ").assign(\n", 684 | " y0 = lambda d: np.round(10 \n", 685 | " + d[\"treated_unit\"]\n", 686 | " + d[\"reg_trend\"]*d[\"trend\"]/2\n", 687 | " + d[\"unit_fe\"] \n", 688 | " + 0.4*d[\"time_fe\"] \n", 689 | " + 2*d[\"reg_fe\"]\n", 690 | " + d[\"w_seas\"]/5, 0),\n", 691 | ").assign(\n", 692 | "# y0 = lambda d: np.round(d[\"y0\"] + d.groupby(\"city\")[\"y0\"].shift(1).fillna(0)*0.2, 0)\n", 693 | ").assign(\n", 694 | " y1 = lambda d: d[\"y0\"] + np.minimum(0.2*(np.maximum(0, (pd.to_datetime(d[\"date\"]) - d[\"cohort\"]).dt.days)), 1)*d[\"eff_heter\"]*2\n", 695 | ").assign(\n", 696 | " tau = lambda d: d[\"y1\"] - d[\"y0\"],\n", 697 | " downloads = lambda d: np.where(d[\"treated\"] == 1, d[\"y1\"], d[\"y0\"]) + np.random.normal(0,.7,len(d)),\n", 698 | "# date = lambda d: pd.to_datetime(d[\"date\"]),\n", 699 | ").round({\"downloads\": 0})\n", 700 | "\n", 701 | "# df.head()" 702 | ] 703 | }, 704 | { 705 | "cell_type": "code", 706 | "execution_count": 8, 707 | "metadata": { 708 | "ExecuteTime": { 709 | "end_time": "2023-06-26T13:24:38.347953Z", 710 | "start_time": "2023-06-26T13:24:38.141867Z" 711 | } 712 | }, 713 | "outputs": [], 714 | "source": [ 715 | "# filter only 1st block\n", 716 | "reg_filter = [\"N\", \"S\", \"E\", \"W\"]\n", 717 | "\n", 718 | "mkt_data_all = (df\n", 719 | " .query(\"region.isin(@reg_filter)\")\n", 720 | " .query(\"date.astype('string') <= '2021-06-01'\")\n", 721 | "# .assign(cohort = lambda d: np.where(d[\"cohort\"].astype(str) <= \"2021-06-01\", d[\"cohort\"], \"2050-01-01\"))\n", 722 | " .drop(columns=[\"reg_fe\", \"time_fe\", \"cohort\", \"w_seas\", \"week_day\", \"reg_trend\", \"trend\", \"day\", \"unit_fe\",\n", 723 | " \"y0\", \"y1\", \"eff_heter\", \"reg_ps\", \"treated_unit\",\n", 724 | " ])\n", 725 | " .assign(post=lambda d: (d[\"date\"].astype(str) >= \"2021-05-15\").astype(int))\n", 726 | " .assign(treated=lambda d: d.groupby(\"city\")[\"treated\"].transform(max)))\n", 727 | "\n", 728 | "\n", 729 | "mkt_data = (mkt_data_all.query(\"region=='S'\"))\n", 730 | "\n", 731 | "mkt_data_cohorts = (df\n", 732 | " .assign(post=lambda d: (d[\"date\"] >= d[\"cohort\"]).astype(int))\n", 733 | " .assign(treated=lambda d: d.groupby(\"city\")[\"treated\"].transform(max))\n", 734 | " .drop(columns=[\"reg_fe\", \"time_fe\", \"w_seas\", \"week_day\", \"reg_trend\", \"trend\", \"day\", \"unit_fe\",\n", 735 | " \"y0\", \"y1\", \"eff_heter\"\n", 736 | " , \"reg_ps\", \"treated_unit\"]))\n", 737 | "\n", 738 | "mkt_data_cohorts.to_csv(\"./causal-inference-in-python/data/offline_mkt_staggered.csv\", index=False)\n", 739 | "\n", 740 | "mkt_data_all.to_csv(\"./causal-inference-in-python/data/short_offline_mkt_all_regions.csv\", index=False)\n", 741 | "\n", 742 | "mkt_data.to_csv(\"./causal-inference-in-python/data/short_offline_mkt_south.csv\", index=False)" 743 | ] 744 | }, 745 | { 746 | "cell_type": "markdown", 747 | "metadata": {}, 748 | "source": [ 749 | "# Chapter 9" 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": 13, 755 | "metadata": { 756 | "ExecuteTime": { 757 | "end_time": "2023-01-24T19:59:00.262446Z", 758 | "start_time": "2023-01-24T19:59:00.189182Z" 759 | } 760 | }, 761 | "outputs": [], 762 | "source": [ 763 | "from statsmodels.tsa.arima_process import ArmaProcess\n", 764 | "\n", 765 | "def simulate_series(dates,\n", 766 | " name,\n", 767 | " pop,\n", 768 | " pop_pct,\n", 769 | " ws_w,\n", 770 | " ms_w,\n", 771 | " ys_w,\n", 772 | " ar_w,\n", 773 | " trend_w,\n", 774 | " noise_w,\n", 775 | " ar=1,\n", 776 | " ma=7,\n", 777 | " ):\n", 778 | " \n", 779 | " ws = abs(5-dates.weekday) % 7\n", 780 | " ms = abs(dates.day-10)\n", 781 | " ys = abs(6-dates.month) % 12\n", 782 | " \n", 783 | " arma = ArmaProcess(ar, ma).generate_sample(len(dates))\n", 784 | " trend = np.arange(0, len(dates))\n", 785 | " noise = np.random.normal(0, 1, len(dates))\n", 786 | " \n", 787 | " \n", 788 | " comps = (ws, ms, ys, arma, trend, noise)\n", 789 | " comp_noise = np.random.uniform(0.95, 1.05, len(comps))\n", 790 | " \n", 791 | " coefs = (ws_w, ms_w, ys_w, ar_w, trend_w, noise_w)\n", 792 | " \n", 793 | " result = sum(c*w*noise\n", 794 | " for c, w, noise\n", 795 | " in zip(comps, coefs, comp_noise))*pop*pop_pct*0.005\n", 796 | " \n", 797 | " return pd.Series(result, name=name).clip(0, np.inf).round(0)" 798 | ] 799 | }, 800 | { 801 | "cell_type": "code", 802 | "execution_count": 14, 803 | "metadata": { 804 | "ExecuteTime": { 805 | "end_time": "2023-01-24T19:59:00.271093Z", 806 | "start_time": "2023-01-24T19:59:00.263454Z" 807 | } 808 | }, 809 | "outputs": [], 810 | "source": [ 811 | "br_cities = pd.read_csv(\"./causal-inference-in-python/data/br_cities.csv\")\n", 812 | "\n", 813 | "np.random.seed(3)\n", 814 | "\n", 815 | "states = br_cities[\"state\"].unique()\n", 816 | "pop_pct = np.random.beta(5, 10, len(states))*0.1\n", 817 | "trends = np.random.beta(9, 5, len(states))*0.5-0.25\n", 818 | "week_sas = np.random.uniform(2, 4, len(states))\n", 819 | "m_sas = np.random.uniform(0, 1, len(states))\n", 820 | "y_sas = np.random.uniform(0, 2, len(states))\n", 821 | "\n", 822 | "states_params = pd.DataFrame(dict(\n", 823 | " pop_pct=pop_pct,\n", 824 | " state=states,\n", 825 | " trends=trends,\n", 826 | " week_sas=week_sas,\n", 827 | " m_sas=m_sas,\n", 828 | " y_sas=y_sas,\n", 829 | "))\n", 830 | "\n", 831 | "# states_params.head()" 832 | ] 833 | }, 834 | { 835 | "cell_type": "code", 836 | "execution_count": 15, 837 | "metadata": { 838 | "ExecuteTime": { 839 | "end_time": "2023-01-24T19:59:00.279476Z", 840 | "start_time": "2023-01-24T19:59:00.272275Z" 841 | } 842 | }, 843 | "outputs": [], 844 | "source": [ 845 | "br_cities = pd.read_csv(\"./causal-inference-in-python/data/br_cities.csv\").merge(states_params, on=\"state\")" 846 | ] 847 | }, 848 | { 849 | "cell_type": "code", 850 | "execution_count": 16, 851 | "metadata": { 852 | "ExecuteTime": { 853 | "end_time": "2023-01-24T19:59:00.456232Z", 854 | "start_time": "2023-01-24T19:59:00.280636Z" 855 | } 856 | }, 857 | "outputs": [], 858 | "source": [ 859 | "np.random.seed(12345)\n", 860 | "\n", 861 | "date = pd.date_range(\"2022-03-01\", \"2022-06-30\", freq=\"D\")\n", 862 | "\n", 863 | "series = [\n", 864 | " simulate_series(date,\n", 865 | " name=city[\"city\"],\n", 866 | " pop=city[\"pop\"]*city[\"pop_pct\"],\n", 867 | " pop_pct=city[\"pop_pct\"],\n", 868 | " ws_w=city[\"week_sas\"],\n", 869 | " ms_w=city[\"m_sas\"],\n", 870 | " ys_w=city[\"y_sas\"],\n", 871 | " ar_w=0.1,\n", 872 | " trend_w=city[\"trends\"],\n", 873 | " noise_w=1000/np.sqrt(city[\"pop\"]*city[\"pop_pct\"])\n", 874 | " ).round(0).to_frame().assign(\n", 875 | " population=city[\"pop\"], city=city[\"city\"], state=city[\"state\"], date=date,\n", 876 | " ).rename(columns={city[\"city\"]:\"app_download\"})\n", 877 | " for _, city in br_cities.sort_values(\"pop\", ascending=False).head(50).iterrows()\n", 878 | "]" 879 | ] 880 | }, 881 | { 882 | "cell_type": "code", 883 | "execution_count": 17, 884 | "metadata": { 885 | "ExecuteTime": { 886 | "end_time": "2023-01-24T19:59:00.481510Z", 887 | "start_time": "2023-01-24T19:59:00.457362Z" 888 | } 889 | }, 890 | "outputs": [], 891 | "source": [ 892 | "features = pd.concat([pd.DataFrame(dict(\n", 893 | " activity=(ArmaProcess([1, 0.9, 0.8, 0.7], 7).generate_sample(len(date))+row[\"hdi\"]).round(0)*3,\n", 894 | " date=date,\n", 895 | " state=row[\"state\"],\n", 896 | " city=row[\"city\"])) \n", 897 | " for _, row in br_cities.sort_values(\"pop\", ascending=False).head(50).iterrows()])\n", 898 | "\n" 899 | ] 900 | }, 901 | { 902 | "cell_type": "code", 903 | "execution_count": 18, 904 | "metadata": { 905 | "ExecuteTime": { 906 | "end_time": "2023-01-24T19:59:00.486234Z", 907 | "start_time": "2023-01-24T19:59:00.482811Z" 908 | } 909 | }, 910 | "outputs": [], 911 | "source": [ 912 | "def simulate_effect(df, city_col, date_col, y_col, city, start_at, window, effect_pct):\n", 913 | " \n", 914 | " window_mask = (df[date_col] >= start_at) & (df[city_col] == city)\n", 915 | " \n", 916 | " def rbf(x, center, sigma):\n", 917 | " return np.exp(-(x-center)**2 / sigma**2)\n", 918 | "\n", 919 | " kernel = rbf((df[date_col] - pd.to_datetime(start_at)).dt.days - window//2 - 1, 0, window/2)\n", 920 | " \n", 921 | " \n", 922 | " y = np.where(window_mask, df[y_col] + df[y_col]*effect_pct*kernel, df[y_col])\n", 923 | " \n", 924 | " return df.assign(**{y_col: y})\n" 925 | ] 926 | }, 927 | { 928 | "cell_type": "code", 929 | "execution_count": 19, 930 | "metadata": { 931 | "ExecuteTime": { 932 | "end_time": "2023-01-24T19:59:00.542421Z", 933 | "start_time": "2023-01-24T19:59:00.487636Z" 934 | } 935 | }, 936 | "outputs": [], 937 | "source": [ 938 | "df = pd.concat(series)\n", 939 | "\n", 940 | "df = simulate_effect(df, \"city\", \"date\", \"app_download\", \"sao_paulo\", \"2022-05-01\", 40, 0.4)\n", 941 | "df = simulate_effect(df, \"city\", \"date\", \"app_download\", \"joao_pessoa\", \"2022-05-01\", 40, 0.5)\n", 942 | "df = simulate_effect(df, \"city\", \"date\", \"app_download\", \"porto_alegre\", \"2022-05-01\", 40, 0.6)\n", 943 | "\n", 944 | "df = df.assign(\n", 945 | " post = (df[\"date\"] >= \"2022-05-01\")*1,\n", 946 | " treated = (df[\"city\"].isin([\"sao_paulo\", \"joao_pessoa\", \"porto_alegre\"]))*1,\n", 947 | ")\n", 948 | "\n", 949 | "df.to_csv(\"./causal-inference-in-python/data/online_mkt.csv\", index=False)" 950 | ] 951 | }, 952 | { 953 | "cell_type": "code", 954 | "execution_count": 20, 955 | "metadata": { 956 | "ExecuteTime": { 957 | "end_time": "2023-01-24T19:59:00.602696Z", 958 | "start_time": "2023-01-24T19:59:00.543585Z" 959 | } 960 | }, 961 | "outputs": [], 962 | "source": [ 963 | "np.random.seed(123)\n", 964 | "\n", 965 | "df_norm = df.assign(app_download_pct = 100*df[\"app_download\"]/df[\"population\"])\n", 966 | "\n", 967 | "\n", 968 | "df_norm_cov = (df_norm\n", 969 | " .merge(br_cities[[\"city\", \"state\", \"hdi\"]])\n", 970 | " .assign(comp_download_pct = lambda d: 100*(0.8*d[\"app_download\"]+d[\"hdi\"])/d[\"population\"])\n", 971 | " .drop(columns=[\"hdi\"]))\n", 972 | "\n", 973 | "df_norm_cov.to_csv(\"./causal-inference-in-python/data/online_mkt_cov.csv\", index=False)" 974 | ] 975 | }, 976 | { 977 | "cell_type": "markdown", 978 | "metadata": {}, 979 | "source": [ 980 | "# Chapter 10" 981 | ] 982 | }, 983 | { 984 | "cell_type": "code", 985 | "execution_count": 8, 986 | "metadata": { 987 | "ExecuteTime": { 988 | "end_time": "2023-02-06T12:48:17.398154Z", 989 | "start_time": "2023-02-06T12:48:17.373468Z" 990 | } 991 | }, 992 | "outputs": [], 993 | "source": [ 994 | "import numpy as np\n", 995 | "import pandas as pd\n", 996 | "from matplotlib import pyplot as plt\n", 997 | "from statsmodels.tsa.arima_process import ArmaProcess\n", 998 | "\n", 999 | "T = 120\n", 1000 | "m = 2\n", 1001 | "p = 0.5\n", 1002 | "\n", 1003 | "\n", 1004 | "def y_given_d(d, effect_params=[3,2,1], T=T, seed=None):\n", 1005 | " np.random.seed(seed)\n", 1006 | " x = np.arange(1, T+1)\n", 1007 | " return (np.log(x+1)\n", 1008 | " + 2*np.sin(x*2*np.pi/24)\n", 1009 | " + np.convolve(~d.astype(bool), effect_params)[:-(len(effect_params)-1)]\n", 1010 | " + np.random.normal(0, 1, T)\n", 1011 | "# + ArmaProcess([3,2,], 3).generate_sample(T)\n", 1012 | " ).round(2)\n", 1013 | "\n", 1014 | "\n", 1015 | "# correct tau = 3+2+1=6\n", 1016 | "effect_params = [3,2,1]\n", 1017 | "\n", 1018 | "\n", 1019 | "np.random.seed(123)\n", 1020 | "\n", 1021 | "df_sb_every = pd.DataFrame(\n", 1022 | " dict(\n", 1023 | " d=np.random.binomial(1, 0.5, T),\n", 1024 | " )\n", 1025 | ").assign(\n", 1026 | " delivery_time = lambda d: y_given_d(d[\"d\"], seed=1),\n", 1027 | " delivery_time_1 = lambda d: y_given_d(np.ones(T), seed=1),\n", 1028 | " delivery_time_0 = lambda d: y_given_d(np.zeros(T), seed=1),\n", 1029 | ").assign(\n", 1030 | " tau = lambda d: d[\"delivery_time_1\"] - d[\"delivery_time_0\"]\n", 1031 | ")\n", 1032 | "\n", 1033 | "\n", 1034 | "df_sb_every.to_csv(\"./causal-inference-in-python/data/sb_exp_every.csv\", index=False)" 1035 | ] 1036 | }, 1037 | { 1038 | "cell_type": "code", 1039 | "execution_count": 4, 1040 | "metadata": { 1041 | "ExecuteTime": { 1042 | "end_time": "2023-02-06T12:37:42.355708Z", 1043 | "start_time": "2023-02-06T12:37:42.333596Z" 1044 | } 1045 | }, 1046 | "outputs": [], 1047 | "source": [ 1048 | "def gen_d(rand_points, p):\n", 1049 | " result = [np.random.binomial(1, p)]\n", 1050 | " \n", 1051 | " for t in rand_points[1:]:\n", 1052 | " result.append(np.random.binomial(1, p)*t + (1-t)*result[-1])\n", 1053 | " \n", 1054 | " return np.array(result)\n", 1055 | "\n", 1056 | "\n", 1057 | "m = 2\n", 1058 | "T = 120\n", 1059 | "p = 0.5\n", 1060 | "\n", 1061 | "n = T//m \n", 1062 | "\n", 1063 | "rand_points_opt = np.isin(\n", 1064 | " np.arange(1, T+1),\n", 1065 | " [1] + [i*m+1 for i in range(2, int(n)-1)]\n", 1066 | ")\n", 1067 | "\n", 1068 | "np.random.seed(1)\n", 1069 | "\n", 1070 | "d_opt = gen_d(rand_points_opt, p)\n", 1071 | "y_opt = y_given_d(d_opt, T=T, seed=1)\n", 1072 | "\n", 1073 | "pd.DataFrame(dict(rand_points=rand_points_opt,\n", 1074 | " d=d_opt,\n", 1075 | " delivery_time=y_opt)).to_csv(\"./causal-inference-in-python/data/sb_exp_opt.csv\", index=False)" 1076 | ] 1077 | }, 1078 | { 1079 | "cell_type": "markdown", 1080 | "metadata": {}, 1081 | "source": [ 1082 | "# Chapter 11" 1083 | ] 1084 | }, 1085 | { 1086 | "cell_type": "code", 1087 | "execution_count": 21, 1088 | "metadata": { 1089 | "ExecuteTime": { 1090 | "end_time": "2023-01-24T19:59:00.655823Z", 1091 | "start_time": "2023-01-24T19:59:00.603890Z" 1092 | } 1093 | }, 1094 | "outputs": [], 1095 | "source": [ 1096 | "n=10000\n", 1097 | "np.random.seed(123)\n", 1098 | "\n", 1099 | "# confounders\n", 1100 | "age = 18 + np.random.normal(24, 4, n).round(1)\n", 1101 | "income = 500 + np.random.gamma(1, age * 100, n).round(0)\n", 1102 | "credit_score = (np.random.beta(1,3, n)*1000).round(0)\n", 1103 | "\n", 1104 | "\n", 1105 | "u = np.random.uniform(-1, 1, n)\n", 1106 | "\n", 1107 | "categs = [\"never-taker\", \"complier\"]\n", 1108 | "\n", 1109 | "cutoff=0.6\n", 1110 | "e = (1/(1+np.exp(-(u-0.05*age+0.01*credit_score))))\n", 1111 | "cust_categ = np.select([e <= cutoff, e > cutoff], categs)\n", 1112 | "\n", 1113 | "# plt.hist(e)\n", 1114 | "\n", 1115 | "# Instrument\n", 1116 | "prime_elegible = np.random.binomial(1, 0.5, n)\n", 1117 | "choose_prime = (\n", 1118 | " ((cust_categ == \"complier\") & (prime_elegible == 1))\n", 1119 | ").astype(int)\n", 1120 | "\n", 1121 | "# outcome\n", 1122 | "group_effect = np.select([cust_categ == \"complier\", cust_categ == \"never-taker\"],\n", 1123 | " [700, 200])\n", 1124 | "\n", 1125 | "pv_0 = np.random.normal(200 + 0.4*income + 10*age - 500*u, 200).round(2)\n", 1126 | "pv_1 = pv_0 + group_effect\n", 1127 | "\n", 1128 | "tau = pv_1 - pv_0\n", 1129 | "pv = pv_0 + group_effect*choose_prime\n", 1130 | "\n", 1131 | "\n", 1132 | "\n", 1133 | "df = pd.DataFrame(dict(categ=cust_categ,\n", 1134 | " age=age,\n", 1135 | " income=income,\n", 1136 | " credit_score=credit_score,\n", 1137 | " prime_elegible=prime_elegible,\n", 1138 | " prime_card=choose_prime,\n", 1139 | " pv=pv,\n", 1140 | " tau=tau,))[\n", 1141 | " ['age', 'income', 'credit_score', 'prime_elegible',\n", 1142 | " 'prime_card', 'pv', 'tau', 'categ']\n", 1143 | "]\n", 1144 | "\n", 1145 | "df.to_csv(\"./causal-inference-in-python/data/prime_card.csv\", index=False)" 1146 | ] 1147 | }, 1148 | { 1149 | "cell_type": "code", 1150 | "execution_count": null, 1151 | "metadata": {}, 1152 | "outputs": [], 1153 | "source": [] 1154 | } 1155 | ], 1156 | "metadata": { 1157 | "kernelspec": { 1158 | "display_name": "Python [conda env:root] *", 1159 | "language": "python", 1160 | "name": "conda-root-py" 1161 | }, 1162 | "language_info": { 1163 | "codemirror_mode": { 1164 | "name": "ipython", 1165 | "version": 3 1166 | }, 1167 | "file_extension": ".py", 1168 | "mimetype": "text/x-python", 1169 | "name": "python", 1170 | "nbconvert_exporter": "python", 1171 | "pygments_lexer": "ipython3", 1172 | "version": "3.7.12" 1173 | }, 1174 | "toc": { 1175 | "base_numbering": 1, 1176 | "nav_menu": {}, 1177 | "number_sections": true, 1178 | "sideBar": true, 1179 | "skip_h1_title": false, 1180 | "title_cell": "Table of Contents", 1181 | "title_sidebar": "Contents", 1182 | "toc_cell": false, 1183 | "toc_position": {}, 1184 | "toc_section_display": true, 1185 | "toc_window_display": false 1186 | }, 1187 | "varInspector": { 1188 | "cols": { 1189 | "lenName": 16, 1190 | "lenType": 16, 1191 | "lenVar": 40 1192 | }, 1193 | "kernels_config": { 1194 | "python": { 1195 | "delete_cmd_postfix": "", 1196 | "delete_cmd_prefix": "del ", 1197 | "library": "var_list.py", 1198 | "varRefreshCmd": "print(var_dic_list())" 1199 | }, 1200 | "r": { 1201 | "delete_cmd_postfix": ") ", 1202 | "delete_cmd_prefix": "rm(", 1203 | "library": "var_list.r", 1204 | "varRefreshCmd": "cat(var_dic_list()) " 1205 | } 1206 | }, 1207 | "types_to_exclude": [ 1208 | "module", 1209 | "function", 1210 | "builtin_function_or_method", 1211 | "instance", 1212 | "_Feature" 1213 | ], 1214 | "window_display": false 1215 | } 1216 | }, 1217 | "nbformat": 4, 1218 | "nbformat_minor": 2 1219 | } 1220 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/book_cover.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CausalInferenceLab/causal-inference-in-python-code/a87f67562c482f190a1c50c1628ba81a137c503d/실무로 통하는 인과추론 with 파이썬/data/book_cover.jpg -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/cross_sell_email.csv: -------------------------------------------------------------------------------- 1 | gender,cross_sell_email,age,conversion 2 | 0,short,15,0 3 | 1,short,27,0 4 | 1,long,17,0 5 | 1,long,34,0 6 | 1,no_email,14,0 7 | 0,no_email,17,1 8 | 1,long,16,0 9 | 1,short,19,1 10 | 1,short,17,1 11 | 0,no_email,17,0 12 | 1,no_email,27,0 13 | 1,short,22,0 14 | 1,short,16,0 15 | 1,long,28,0 16 | 0,short,26,0 17 | 0,long,15,0 18 | 1,no_email,18,0 19 | 1,short,26,0 20 | 0,long,18,0 21 | 1,no_email,16,0 22 | 1,long,21,0 23 | 1,no_email,17,0 24 | 1,short,23,0 25 | 1,short,44,0 26 | 0,no_email,22,0 27 | 0,short,26,0 28 | 1,short,42,0 29 | 0,long,35,0 30 | 0,no_email,16,0 31 | 0,no_email,26,0 32 | 1,long,35,0 33 | 0,long,16,0 34 | 0,long,25,0 35 | 1,short,31,0 36 | 1,short,25,0 37 | 1,short,15,0 38 | 1,short,20,0 39 | 1,no_email,28,0 40 | 0,short,15,0 41 | 0,long,17,0 42 | 1,long,19,0 43 | 0,long,29,0 44 | 1,long,16,0 45 | 1,short,18,0 46 | 1,short,15,0 47 | 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1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,68.27 33 | 0,,,,,,,0,1.0,83.3 34 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,56.7 35 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,73.3 36 | 1,,,,,,,0,0.0,73.3 37 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,68.27 38 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,74.97 39 | 1,,,,,,,1,0.0,66.62 40 | 0,,,,,,,0,0.0,66.69 41 | 0,,,,,,,0,1.0,81.63 42 | 1,,,,,,,0,1.0,56.6 43 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,76.64 44 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.74 45 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,65.04 46 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,84.99 47 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,96.66 48 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.33 49 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.31 50 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,76.64 51 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,68.31 52 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,71.61 53 | 0,,,,,,,0,0.0,60.01 54 | 0,,,,,,,0,1.0,61.72 55 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,79.96 56 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.31 57 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,63.26 58 | 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1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,73.28 85 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,73.3 86 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,91.69 87 | 1,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,69.98 88 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,83.32 89 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,54.949980000000004 90 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,70.0 91 | 1,,,,,,,0,1.0,51.66 92 | 1,,,,,,,0,0.0,81.63 93 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,81.72 94 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,90.05 95 | 0,,,,,,,1,0.0,51.67998 96 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,73.3 97 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,76.64 98 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,58.34 99 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,78.31 100 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,83.3 101 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,51.678999999999995 102 | 0,,,,,,,0,0.0,61.67997 103 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,76.66 104 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,56.70997 105 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,79.99996999999999 106 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,79.98 107 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,73.3 108 | 0,,,,,,,0,0.0,73.39 109 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,71.64996 110 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,79.97995 111 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,71.62997 112 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,79.98 113 | 0,,,,,,,0,0.0,86.64 114 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.39 115 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,64.979 116 | 1,,,,,,,1,0.0,93.39 117 | 0,,,,,,,0,0.0,70.03 118 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,84.97 119 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,89.98 120 | 1,,,,,,,1,0.0,56.65 121 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,78.34995 122 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,74.90996 123 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,78.31 124 | 0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,81.7 125 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,66.64 126 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,86.64 127 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,79.98 128 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.65 129 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,84.97 130 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,76.71 131 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,60.08 132 | 0,,,,,,,0,1.0,91.65 133 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,75.06 134 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,80.05 135 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,81.67 136 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,86.65994 137 | 1,,,,,,,0,1.0,83.3 138 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,74.95997 139 | 0,,,,,,,0,0.0,69.95996 140 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,88.32994000000001 141 | 0,,,,,,,1,0.0,64.95 142 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,66.64 143 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,76.64 144 | 0,,,,,,,1,0.0,74.99 145 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,59.94 146 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,75.02 147 | 0,,,,,,,1,0.0,58.31 148 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,76.76 149 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,71.68 150 | 1,,,,,,,0,0.0,89.98 151 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,84.99 152 | 1,,,,,,,0,0.0,81.63 153 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,83.32 154 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,73.31997 155 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,73.27996999999999 156 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,88.38 157 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,68.36 158 | 0,,,,,,,0,1.0,68.29 159 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,71.63 160 | 1,,,,,,,0,1.0,71.63 161 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,91.65 162 | 1,,,,,,,0,0.0,68.31 163 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,75.02 164 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,73.44 165 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,50.1 166 | 1,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,63.31998000000001 167 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,70.03 168 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,73.32 169 | 0,,,,,,,1,0.0,83.3 170 | 1,,,,,,,1,0.0,75.06 171 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,86.66 172 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,68.32996 173 | 0,,,,,,,0,1.0,78.329 174 | 0,,,,,,,1,0.0,68.31 175 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,75.02 176 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,78.33 177 | 0,,,,,,,0,1.0,73.28 178 | 0,,,,,,,0,0.0,74.97 179 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,70.05 180 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,76.63996999999999 181 | 1,,,,,,,0,1.0,84.99 182 | 0,,,,,,,0,1.0,71.74 183 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,88.33 184 | 0,,,,,,,1,0.0,63.28 185 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,91.74 186 | 1,,,,,,,1,0.0,0.0 187 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,79.98 188 | 1,,,,,,,0,0.0,69.94 189 | 1,0.0,0.0,0.0,1.0,0.0,0.0,0,1.0,63.27997 190 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,84.99 191 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,73.3 192 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.33 193 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.29997 194 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,84.97 195 | 1,,,,,,,1,0.0,81.65 196 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.3 197 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,89.99994000000001 198 | 0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,79.93996 199 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,78.29 200 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,86.64 201 | 1,,,,,,,0,0.0,91.67 202 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,76.75 203 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,73.28 204 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,70.03 205 | 1,,,,,,,0,0.0,83.32 206 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,90.0 207 | 1,,,,,,,0,1.0,83.32 208 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.3 209 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,78.31 210 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,88.33 211 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.37 212 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,86.66 213 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,86.66 214 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,84.99 215 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,88.33 216 | 1,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,88.33 217 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,81.65 218 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,78.229 219 | 1,,,,,,,0,1.0,76.73 220 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,89.98 221 | 1,,,,,,,0,1.0,74.95 222 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,75.02 223 | 0,,,,,,,0,1.0,56.759980000000006 224 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.669 225 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,58.27 226 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,81.63 227 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,58.299969999999995 228 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,78.38 229 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.72 230 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,84.99 231 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,81.67 232 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,86.66 233 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.31 234 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,65.02 235 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,80.0 236 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,78.37995 237 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,86.66 238 | 1,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,83.34 239 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,84.99 240 | 0,,,,,,,0,1.0,89.98 241 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,66.69 242 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,83.37 243 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,88.33 244 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,86.64 245 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,90.0 246 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,84.97 247 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,73.32 248 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,85.01 249 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,75.06 250 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,81.65 251 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,68.27 252 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,83.34 253 | 1,,,,,,,1,0.0,80.05 254 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,86.64 255 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.65 256 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,68.36 257 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,80.0 258 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,73.35 259 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,68.36 260 | 0,,,,,,,0,1.0,61.63 261 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,56.71 262 | 1,,,,,,,0,1.0,78.31 263 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,90.0 264 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,70.03 265 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,68.28996 266 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,76.73 267 | 1,,,,,,,1,0.0,86.65996 268 | 0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,48.33998 269 | 1,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,81.7 270 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,81.72 271 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,56.59996999999999 272 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,81.65 273 | 1,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,94.99 274 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,59.92 275 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,61.68 276 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,86.64 277 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,86.66 278 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,88.31 279 | 1,,,,,,,1,0.0,81.7 280 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,68.34 281 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,78.4 282 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,74.97 283 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,83.32 284 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,84.99 285 | 1,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,76.62 286 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,68.36 287 | 1,,,,,,,0,1.0,76.62 288 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,66.62 289 | 0,,,,,,,1,0.0,74.90997 290 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,79.96 291 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,88.33 292 | 0,,,,,,,0,1.0,76.71 293 | 1,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,65.02 294 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,83.32 295 | 1,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,86.75 296 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,83.44 297 | 1,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,95.01 298 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,69.99996999999999 299 | 0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,86.68 300 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,66.65899999999999 301 | 1,,,,,,,0,1.0,76.62 302 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,71.61 303 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,90.0 304 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,60.01 305 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,94.99 306 | 0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,83.3 307 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,83.32 308 | 1,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,70.0 309 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,73.37 310 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,83.39 311 | 0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,0.0 312 | 1,,,,,,,0,1.0,69.96 313 | 1,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,86.71 314 | 1,,,,,,,0,0.0,90.0 315 | 0,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,61.68 316 | 0,,,,,,,0,0.0,73.37 317 | 1,,,,,,,0,1.0,88.31 318 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,91.65 319 | 1,,,,,,,0,0.0,84.99 320 | 0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,68.36 321 | 1,,,,,,,1,0.0,70.05 322 | 0,,,,,,,1,0.0,66.69 323 | 1,,,,,,,1,0.0,83.29997 324 | 1,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,96.68 325 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/rec_ab_test.csv: -------------------------------------------------------------------------------- 1 | recommender,age,tenure,watch_time 2 | challenger,15,1,2.39 3 | challenger,27,1,2.32 4 | benchmark,17,0,2.74 5 | benchmark,34,1,1.92 6 | benchmark,14,1,2.47 7 | benchmark,17,0,2.04 8 | benchmark,16,2,2.97 9 | challenger,19,3,3.66 10 | challenger,17,2,3.48 11 | benchmark,17,1,1.84 12 | benchmark,27,0,1.56 13 | challenger,22,0,1.38 14 | challenger,16,0,2.09 15 | benchmark,28,1,2.37 16 | challenger,26,0,0.7 17 | benchmark,15,1,2.83 18 | benchmark,18,0,1.2 19 | challenger,26,1,2.12 20 | benchmark,18,0,1.44 21 | benchmark,16,1,2.49 22 | benchmark,21,0,1.79 23 | benchmark,17,1,2.24 24 | challenger,23,0,1.82 25 | challenger,44,4,2.74 26 | benchmark,22,1,1.74 27 | challenger,26,0,1.32 28 | challenger,42,0,0.0 29 | benchmark,35,2,2.62 30 | benchmark,16,0,1.25 31 | benchmark,26,1,1.98 32 | benchmark,35,3,2.85 33 | benchmark,16,0,1.47 34 | benchmark,25,0,2.01 35 | challenger,31,2,2.96 36 | challenger,25,1,2.21 37 | challenger,15,0,1.46 38 | challenger,20,0,2.16 39 | benchmark,28,0,1.75 40 | challenger,15,4,3.61 41 | benchmark,17,1,2.55 42 | benchmark,19,2,3.81 43 | benchmark,29,0,0.94 44 | benchmark,16,0,1.68 45 | challenger,18,0,1.94 46 | challenger,15,0,3.11 47 | challenger,39,2,2.62 48 | challenger,20,3,3.08 49 | challenger,28,2,3.0 50 | benchmark,15,0,1.39 51 | challenger,18,0,0.96 52 | benchmark,27,0,0.9 53 | challenger,22,0,1.72 54 | benchmark,18,1,2.47 55 | challenger,24,0,0.3 56 | challenger,19,0,2.31 57 | challenger,20,0,2.95 58 | benchmark,22,0,2.1 59 | benchmark,14,0,1.92 60 | benchmark,16,0,2.13 61 | challenger,22,0,1.88 62 | benchmark,15,0,2.2 63 | challenger,18,0,2.5 64 | challenger,18,0,2.43 65 | benchmark,17,2,2.36 66 | benchmark,33,2,2.44 67 | challenger,18,0,2.45 68 | benchmark,25,0,1.41 69 | benchmark,22,4,3.99 70 | challenger,15,0,1.97 71 | challenger,15,1,3.39 72 | benchmark,24,1,2.35 73 | benchmark,21,0,2.0 74 | benchmark,32,0,1.21 75 | benchmark,18,3,4.19 76 | benchmark,29,0,0.8 77 | benchmark,21,0,2.42 78 | benchmark,22,0,1.44 79 | benchmark,32,0,1.27 80 | challenger,21,1,3.52 81 | benchmark,21,0,1.7 82 | benchmark,15,0,1.88 83 | benchmark,14,0,1.94 84 | benchmark,23,2,3.37 85 | challenger,17,4,3.95 86 | challenger,19,0,2.62 87 | benchmark,18,1,1.78 88 | benchmark,24,0,1.11 89 | challenger,16,1,3.52 90 | challenger,20,0,2.23 91 | benchmark,18,2,2.65 92 | challenger,15,0,2.45 93 | challenger,19,3,3.61 94 | benchmark,22,0,2.29 95 | benchmark,31,0,2.35 96 | benchmark,17,1,2.89 97 | benchmark,20,0,1.2 98 | benchmark,31,0,1.78 99 | benchmark,18,0,1.56 100 | benchmark,17,3,3.45 101 | benchmark,15,0,2.22 102 | challenger,20,1,2.96 103 | benchmark,19,0,1.79 104 | challenger,20,0,1.49 105 | challenger,18,0,2.48 106 | benchmark,18,4,4.4 107 | benchmark,41,1,1.71 108 | challenger,24,0,1.66 109 | challenger,19,0,0.52 110 | challenger,16,0,0.92 111 | challenger,23,0,1.58 112 | benchmark,23,0,2.38 113 | challenger,16,1,2.08 114 | challenger,26,1,2.57 115 | benchmark,22,0,1.92 116 | benchmark,16,3,3.85 117 | challenger,19,0,2.35 118 | benchmark,15,0,2.71 119 | challenger,17,0,1.46 120 | benchmark,26,1,1.27 121 | challenger,15,2,3.53 122 | challenger,15,1,3.06 123 | benchmark,20,3,3.12 124 | benchmark,18,1,1.91 125 | challenger,20,1,3.17 126 | benchmark,21,0,0.89 127 | challenger,17,1,2.65 128 | benchmark,27,1,2.17 129 | challenger,18,0,3.11 130 | benchmark,21,2,2.75 131 | benchmark,21,1,1.89 132 | benchmark,22,0,1.0 133 | benchmark,18,0,1.6 134 | challenger,14,0,1.98 135 | benchmark,22,0,1.48 136 | challenger,16,0,1.5 137 | benchmark,23,0,1.79 138 | challenger,20,1,2.89 139 | challenger,19,0,1.52 140 | benchmark,19,0,0.88 141 | benchmark,19,1,3.39 142 | challenger,17,1,1.98 143 | challenger,23,1,2.51 144 | benchmark,21,0,2.0 145 | benchmark,15,0,1.96 146 | benchmark,15,0,1.05 147 | benchmark,15,1,2.26 148 | benchmark,16,2,3.13 149 | benchmark,29,0,1.8 150 | challenger,15,0,2.33 151 | benchmark,15,1,3.55 152 | challenger,15,1,2.91 153 | benchmark,14,0,2.22 154 | challenger,17,0,3.15 155 | challenger,14,0,0.87 156 | benchmark,29,0,1.35 157 | benchmark,15,0,1.25 158 | benchmark,24,0,2.06 159 | benchmark,14,2,2.8 160 | benchmark,24,0,1.21 161 | challenger,20,0,1.78 162 | challenger,25,2,2.71 163 | benchmark,16,0,1.51 164 | challenger,23,0,2.45 165 | benchmark,16,0,1.8 166 | benchmark,24,3,2.7 167 | challenger,28,0,0.95 168 | benchmark,24,1,1.67 169 | benchmark,30,2,2.79 170 | benchmark,19,1,3.15 171 | benchmark,18,2,3.39 172 | benchmark,16,0,1.61 173 | benchmark,16,1,2.1 174 | benchmark,15,1,3.1 175 | benchmark,23,0,2.03 176 | benchmark,17,2,2.92 177 | benchmark,18,1,2.53 178 | challenger,22,2,2.24 179 | benchmark,19,1,2.44 180 | challenger,36,0,0.3 181 | benchmark,19,0,1.54 182 | benchmark,18,0,2.51 183 | benchmark,30,0,1.02 184 | benchmark,14,0,2.39 185 | benchmark,26,0,2.07 186 | benchmark,18,0,1.62 187 | challenger,19,0,1.57 188 | challenger,20,0,1.88 189 | benchmark,18,0,2.26 190 | benchmark,18,4,3.94 191 | benchmark,15,0,1.99 192 | challenger,17,0,3.23 193 | challenger,16,0,2.2 194 | benchmark,39,0,1.31 195 | benchmark,17,0,1.79 196 | challenger,24,0,2.0 197 | challenger,17,0,2.26 198 | benchmark,24,1,2.72 199 | challenger,35,0,1.37 200 | benchmark,16,0,1.59 201 | challenger,22,0,1.36 202 | benchmark,21,1,2.3 203 | benchmark,28,1,1.78 204 | challenger,20,1,2.62 205 | challenger,17,0,2.59 206 | benchmark,22,0,1.82 207 | benchmark,25,1,1.79 208 | challenger,15,0,1.57 209 | benchmark,18,1,2.69 210 | benchmark,20,0,0.81 211 | challenger,21,0,1.34 212 | challenger,16,0,2.34 213 | challenger,19,0,2.0 214 | benchmark,18,2,3.47 215 | challenger,18,0,2.42 216 | benchmark,21,4,3.55 217 | benchmark,18,0,2.32 218 | benchmark,21,0,1.27 219 | benchmark,32,0,0.8 220 | benchmark,17,0,1.5 221 | benchmark,19,1,1.97 222 | benchmark,26,0,1.63 223 | benchmark,20,1,2.98 224 | benchmark,25,0,1.44 225 | benchmark,18,0,0.97 226 | benchmark,22,0,1.86 227 | benchmark,24,0,1.38 228 | benchmark,19,0,1.91 229 | benchmark,34,0,0.7 230 | challenger,16,0,2.35 231 | benchmark,15,0,2.07 232 | challenger,20,0,1.83 233 | challenger,17,1,1.9 234 | benchmark,20,0,1.18 235 | challenger,20,0,1.99 236 | benchmark,15,0,2.39 237 | benchmark,20,0,2.87 238 | benchmark,14,0,1.4 239 | benchmark,28,0,0.36 240 | benchmark,17,0,1.55 241 | benchmark,28,1,2.68 242 | benchmark,20,0,1.52 243 | challenger,33,0,1.49 244 | benchmark,20,0,1.69 245 | challenger,20,0,2.54 246 | challenger,15,1,2.36 247 | benchmark,18,0,2.31 248 | challenger,20,0,2.37 249 | challenger,20,0,2.29 250 | challenger,27,3,3.5 251 | benchmark,19,0,2.54 252 | benchmark,31,0,0.73 253 | benchmark,26,0,2.3 254 | challenger,18,0,1.3 255 | benchmark,26,0,1.45 256 | challenger,23,1,3.25 257 | challenger,26,0,1.68 258 | benchmark,27,1,1.37 259 | challenger,16,0,2.19 260 | benchmark,24,0,1.91 261 | benchmark,16,0,2.37 262 | benchmark,18,0,1.87 263 | benchmark,23,0,2.31 264 | benchmark,38,0,0.59 265 | challenger,27,1,2.06 266 | benchmark,27,0,0.69 267 | benchmark,42,0,0.16 268 | benchmark,17,0,1.82 269 | benchmark,16,0,2.44 270 | benchmark,18,0,2.05 271 | challenger,23,0,2.44 272 | benchmark,19,1,1.21 273 | benchmark,21,0,1.94 274 | challenger,19,0,0.9 275 | challenger,24,1,2.26 276 | benchmark,20,0,2.68 277 | benchmark,29,2,2.33 278 | benchmark,30,1,2.79 279 | benchmark,14,0,2.63 280 | benchmark,18,0,2.23 281 | challenger,37,0,0.72 282 | challenger,17,1,3.64 283 | benchmark,15,2,2.42 284 | challenger,23,1,2.52 285 | benchmark,20,0,2.05 286 | benchmark,23,0,2.12 287 | benchmark,21,2,3.43 288 | benchmark,21,0,1.74 289 | benchmark,17,0,1.57 290 | benchmark,18,0,1.56 291 | benchmark,17,4,4.91 292 | benchmark,22,0,1.46 293 | benchmark,24,0,2.2 294 | benchmark,23,0,0.72 295 | benchmark,22,0,3.57 296 | benchmark,26,0,1.31 297 | challenger,35,0,0.75 298 | challenger,19,1,3.94 299 | benchmark,23,0,0.62 300 | benchmark,17,3,3.8 301 | benchmark,29,0,0.41 302 | challenger,19,0,1.65 303 | challenger,26,0,1.86 304 | benchmark,19,1,1.81 305 | benchmark,14,0,1.6 306 | challenger,19,1,2.53 307 | benchmark,27,0,1.48 308 | benchmark,21,2,3.95 309 | benchmark,14,2,2.46 310 | challenger,19,0,2.91 311 | benchmark,15,0,1.31 312 | benchmark,23,1,1.5 313 | challenger,20,0,2.24 314 | challenger,18,1,2.21 315 | challenger,30,0,0.24 316 | challenger,21,1,2.5 317 | benchmark,29,0,2.64 318 | challenger,26,0,1.31 319 | challenger,18,0,1.95 320 | benchmark,18,0,2.13 321 | benchmark,16,0,1.7 322 | benchmark,15,0,1.25 323 | benchmark,16,2,3.31 324 | benchmark,24,1,1.82 325 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/sb_exp_every.csv: -------------------------------------------------------------------------------- 1 | d,delivery_time,delivery_time_1,delivery_time_0,tau 2 | 1,2.84,2.84,5.84,-3.0 3 | 0,4.49,1.49,6.49,-5.0 4 | 0,7.27,2.27,8.27,-6.0 5 | 1,5.27,2.27,8.27,-6.0 6 | 1,5.59,4.59,10.59,-6.0 7 | 0,4.64,1.64,7.64,-6.0 8 | 1,7.76,5.76,11.76,-6.0 9 | 1,4.17,3.17,9.17,-6.0 10 | 0,7.04,4.04,10.04,-5.999999999999999 11 | 0,8.15,3.15,9.15,-6.0 12 | 0,10.46,4.46,10.46,-6.000000000000001 13 | 1,3.5,0.5,6.5,-6.0 14 | 0,5.8,1.8,7.8,-6.0 15 | 0,6.32,1.32,7.32,-6.0 16 | 0,8.49,2.49,8.49,-6.0 17 | 1,3.0,0.0,6.0,-6.0 18 | 0,4.79,0.79,6.79,-6.0 19 | 0,5.07,0.07,6.07,-6.0 20 | 1,4.11,1.11,7.11,-6.0 21 | 1,2.9,1.9,7.9,-6.0 22 | 1,0.58,0.58,6.58,-6.0 23 | 1,3.28,3.28,9.28,-6.0 24 | 1,3.56,3.56,9.56,-6.0 25 | 1,3.72,3.72,9.72,-6.0 26 | 1,4.68,4.68,10.68,-6.0 27 | 0,6.61,3.61,9.61,-6.0 28 | 0,9.62,4.62,10.62,-5.999999999999999 29 | 0,10.16,4.16,10.16,-6.0 30 | 0,11.07,5.07,11.07,-6.0 31 | 1,8.96,5.96,11.96,-6.000000000000001 32 | 0,8.71,4.71,10.71,-6.000000000000001 33 | 0,9.83,4.83,10.83,-6.0 34 | 0,10.25,4.25,10.25,-6.0 35 | 0,9.71,3.71,9.71,-6.000000000000001 36 | 0,9.43,3.43,9.43,-6.0 37 | 0,9.6,3.6,9.6,-6.0 38 | 0,8.0,2.0,8.0,-6.0 39 | 1,5.9,2.9,8.9,-6.0 40 | 1,4.93,3.93,9.93,-6.0 41 | 1,2.72,2.72,8.72,-6.0 42 | 1,1.61,1.61,7.61,-6.0 43 | 0,3.87,0.87,6.87,-6.0 44 | 0,6.11,1.11,7.11,-6.0 45 | 0,9.77,3.77,9.77,-6.0 46 | 1,5.47,2.47,8.47,-6.0 47 | 0,6.21,2.21,8.21,-6.000000000000001 48 | 0,8.54,3.54,9.54,-5.999999999999999 49 | 1,8.99,5.99,11.99,-6.0 50 | 1,5.55,4.55,10.55,-6.000000000000001 51 | 1,5.55,5.55,11.55,-6.000000000000001 52 | 0,8.67,5.67,11.67,-6.0 53 | 1,7.35,5.35,11.35,-6.0 54 | 1,5.78,4.78,10.78,-5.999999999999999 55 | 1,5.66,5.66,11.66,-6.0 56 | 0,8.75,5.75,11.75,-6.0 57 | 0,11.36,6.36,12.36,-5.999999999999999 58 | 0,12.31,6.31,12.31,-6.000000000000001 59 | 1,9.01,6.01,12.01,-6.0 60 | 1,5.9,4.9,10.9,-6.0 61 | 1,5.0,5.0,11.0,-6.0 62 | 1,2.86,2.86,8.86,-6.0 63 | 1,4.4,4.4,10.4,-6.0 64 | 1,3.26,3.26,9.26,-6.0 65 | 1,2.14,2.14,8.14,-6.0 66 | 1,2.75,2.75,8.75,-6.0 67 | 0,5.13,2.13,8.13,-6.000000000000001 68 | 1,5.42,3.42,9.42,-6.0 69 | 0,8.02,4.02,10.02,-6.0 70 | 0,10.02,5.02,11.02,-6.0 71 | 1,4.87,1.87,7.87,-6.0 72 | 0,6.31,2.31,8.31,-6.0 73 | 1,5.79,3.79,9.79,-5.999999999999999 74 | 1,5.98,4.98,10.98,-6.0 75 | 1,6.19,6.19,12.19,-5.999999999999999 76 | 0,9.06,6.06,12.06,-6.000000000000001 77 | 1,6.05,4.05,10.05,-6.000000000000001 78 | 1,6.98,5.98,11.98,-6.0 79 | 0,10.2,7.2,13.2,-5.999999999999999 80 | 0,11.54,6.54,12.54,-5.999999999999999 81 | 1,9.89,6.89,12.89,-6.000000000000001 82 | 0,9.6,5.6,11.6,-6.0 83 | 1,7.22,5.22,11.22,-6.000000000000001 84 | 1,6.14,5.14,11.14,-6.000000000000001 85 | 0,7.85,4.85,10.85,-6.0 86 | 1,6.14,4.14,10.14,-6.000000000000001 87 | 1,4.58,3.58,9.58,-6.0 88 | 0,5.39,2.39,8.39,-6.0 89 | 0,8.13,3.13,9.13,-6.000000000000001 90 | 0,8.69,2.69,8.69,-6.0 91 | 0,9.64,3.64,9.64,-6.0 92 | 1,6.79,3.79,9.79,-5.999999999999999 93 | 1,3.99,2.99,8.99,-6.0 94 | 0,5.75,2.75,8.75,-6.0 95 | 1,4.92,2.92,8.92,-6.0 96 | 1,5.47,4.47,10.47,-6.000000000000001 97 | 1,4.65,4.65,10.65,-6.0 98 | 0,7.76,4.76,10.76,-6.0 99 | 0,10.64,5.64,11.64,-6.000000000000001 100 | 0,11.4,5.4,11.4,-6.0 101 | 0,13.05,7.05,13.05,-6.000000000000001 102 | 1,9.11,6.11,12.11,-5.999999999999999 103 | 1,8.86,7.86,13.86,-5.999999999999999 104 | 0,9.98,6.98,12.98,-6.0 105 | 0,11.98,6.98,12.98,-6.0 106 | 0,10.98,4.98,10.98,-6.0 107 | 1,8.84,5.84,11.84,-6.0 108 | 1,6.94,5.94,11.94,-5.999999999999999 109 | 1,3.74,3.74,9.74,-6.0 110 | 1,3.92,3.92,9.92,-6.0 111 | 0,6.74,3.74,9.74,-6.0 112 | 0,6.93,1.93,7.93,-6.0 113 | 0,9.31,3.31,9.31,-6.0 114 | 0,9.65,3.65,9.65,-6.0 115 | 1,4.89,1.89,7.89,-6.0 116 | 0,7.17,3.17,9.17,-6.0 117 | 1,3.72,1.72,7.72,-6.0 118 | 1,4.32,3.32,9.32,-6.0 119 | 1,2.16,2.16,8.16,-6.0 120 | 0,8.39,5.39,11.39,-6.000000000000001 121 | 1,7.2,5.2,11.2,-5.999999999999999 122 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/sb_exp_opt.csv: -------------------------------------------------------------------------------- 1 | rand_points,d,delivery_time 2 | True,0,5.84 3 | False,0,5.4 4 | False,0,8.86 5 | False,0,8.79 6 | True,0,10.93 7 | False,0,7.02 8 | True,0,13.3 9 | False,0,7.95 10 | True,0,10.26 11 | False,0,9.45 12 | True,0,10.25 13 | False,0,5.66 14 | True,0,9.32 15 | False,0,7.49 16 | True,0,8.24 17 | False,0,5.49 18 | True,0,7.57 19 | False,0,6.2 20 | True,0,7.4 21 | False,0,7.92 22 | True,1,3.06 23 | False,1,5.16 24 | True,0,5.75 25 | False,0,7.99 26 | True,1,7.8 27 | False,1,4.01 28 | True,0,8.14 29 | False,0,9.33 30 | True,0,11.45 31 | False,0,12.15 32 | True,0,10.19 33 | False,0,11.4 34 | True,1,7.54 35 | False,1,4.92 36 | True,1,3.99 37 | False,1,3.89 38 | True,1,1.86 39 | False,1,3.65 40 | True,0,6.7 41 | False,0,6.45 42 | True,1,4.5 43 | False,1,2.26 44 | True,0,4.54 45 | False,0,9.14 46 | True,0,7.19 47 | False,0,8.34 48 | True,1,7.29 49 | False,1,6.54 50 | True,0,6.14 51 | False,0,10.86 52 | True,0,11.48 53 | False,0,10.97 54 | True,0,11.23 55 | False,0,12.36 56 | True,0,11.74 57 | False,0,12.39 58 | True,1,8.98 59 | False,1,6.47 60 | True,1,4.47 61 | False,1,4.96 62 | True,0,5.34 63 | False,0,9.96 64 | True,1,5.47 65 | False,1,2.72 66 | True,0,6.32 67 | False,0,6.66 68 | True,1,6.43 69 | False,1,4.44 70 | True,1,4.01 71 | False,1,0.77 72 | True,1,3.59 73 | False,1,4.73 74 | True,0,7.74 75 | False,0,11.1 76 | True,0,11.57 77 | False,0,9.91 78 | True,1,10.48 79 | False,1,8.12 80 | True,1,5.64 81 | False,1,7.08 82 | True,1,5.18 83 | False,1,5.32 84 | True,1,5.41 85 | False,1,4.58 86 | True,0,6.89 87 | False,0,8.57 88 | True,0,8.34 89 | False,0,9.58 90 | True,1,5.38 91 | False,1,4.49 92 | True,0,6.2 93 | False,0,7.25 94 | True,0,8.92 95 | False,0,9.24 96 | True,0,10.75 97 | False,0,10.29 98 | True,1,7.72 99 | False,1,6.99 100 | True,0,8.26 101 | False,0,12.42 102 | True,0,11.65 103 | False,0,14.11 104 | True,1,9.3 105 | False,1,7.67 106 | True,1,4.88 107 | False,1,6.54 108 | True,0,8.76 109 | False,0,8.08 110 | True,1,7.81 111 | False,1,4.86 112 | True,0,4.62 113 | False,0,9.38 114 | True,1,6.29 115 | False,1,2.1 116 | True,0,7.11 117 | False,0,6.41 118 | True,1,6.97 119 | False,1,3.29 120 | False,1,6.18 121 | False,1,4.43 122 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/short_offline_mkt_south.csv: -------------------------------------------------------------------------------- 1 | date,city,region,treated,tau,downloads,post 2 | 2021-05-01,5,S,0,0.0,51.0,0 3 | 2021-05-02,5,S,0,0.0,51.0,0 4 | 2021-05-03,5,S,0,0.0,51.0,0 5 | 2021-05-04,5,S,0,0.0,50.0,0 6 | 2021-05-05,5,S,0,0.0,49.0,0 7 | 2021-05-06,5,S,0,0.0,51.0,0 8 | 2021-05-07,5,S,0,0.0,51.0,0 9 | 2021-05-08,5,S,0,0.0,50.0,0 10 | 2021-05-09,5,S,0,0.0,52.0,0 11 | 2021-05-10,5,S,0,0.0,51.0,0 12 | 2021-05-11,5,S,0,0.0,51.0,0 13 | 2021-05-12,5,S,0,0.0,50.0,0 14 | 2021-05-13,5,S,0,0.0,51.0,0 15 | 2021-05-14,5,S,0,0.0,50.0,0 16 | 2021-05-15,5,S,0,0.0,50.0,1 17 | 2021-05-16,5,S,0,0.0,49.0,1 18 | 2021-05-17,5,S,0,0.0,52.0,1 19 | 2021-05-18,5,S,0,0.0,50.0,1 20 | 2021-05-19,5,S,0,0.0,49.0,1 21 | 2021-05-20,5,S,0,0.0,51.0,1 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2021-05-26,71,S,1,0.08909264360219282,53.0,1 604 | 2021-05-27,71,S,1,0.08909264360219282,50.0,1 605 | 2021-05-28,71,S,1,0.08909264360219282,51.0,1 606 | 2021-05-29,71,S,1,0.08909264360219282,50.0,1 607 | 2021-05-30,71,S,1,0.08909264360219282,51.0,1 608 | 2021-05-31,71,S,1,0.08909264360219282,53.0,1 609 | 2021-06-01,71,S,1,0.08909264360219282,52.0,1 610 | 2021-05-01,76,S,0,0.0,52.0,0 611 | 2021-05-02,76,S,0,0.0,52.0,0 612 | 2021-05-03,76,S,0,0.0,53.0,0 613 | 2021-05-04,76,S,0,0.0,53.0,0 614 | 2021-05-05,76,S,0,0.0,52.0,0 615 | 2021-05-06,76,S,0,0.0,54.0,0 616 | 2021-05-07,76,S,0,0.0,54.0,0 617 | 2021-05-08,76,S,0,0.0,53.0,0 618 | 2021-05-09,76,S,0,0.0,52.0,0 619 | 2021-05-10,76,S,0,0.0,54.0,0 620 | 2021-05-11,76,S,0,0.0,52.0,0 621 | 2021-05-12,76,S,0,0.0,53.0,0 622 | 2021-05-13,76,S,0,0.0,50.0,0 623 | 2021-05-14,76,S,0,0.0,52.0,0 624 | 2021-05-15,76,S,0,0.0,53.0,1 625 | 2021-05-16,76,S,0,0.0,52.0,1 626 | 2021-05-17,76,S,0,0.0,54.0,1 627 | 2021-05-18,76,S,0,0.0,52.0,1 628 | 2021-05-19,76,S,0,0.0,53.0,1 629 | 2021-05-20,76,S,0,0.0,54.0,1 630 | 2021-05-21,76,S,0,0.0,53.0,1 631 | 2021-05-22,76,S,0,0.0,54.0,1 632 | 2021-05-23,76,S,0,0.0,53.0,1 633 | 2021-05-24,76,S,0,0.0,54.0,1 634 | 2021-05-25,76,S,0,0.0,54.0,1 635 | 2021-05-26,76,S,0,0.0,53.0,1 636 | 2021-05-27,76,S,0,0.0,54.0,1 637 | 2021-05-28,76,S,0,0.0,52.0,1 638 | 2021-05-29,76,S,0,0.0,52.0,1 639 | 2021-05-30,76,S,0,0.0,53.0,1 640 | 2021-05-31,76,S,0,0.0,54.0,1 641 | 2021-06-01,76,S,0,0.0,53.0,1 642 | 2021-05-01,78,S,0,0.0,51.0,0 643 | 2021-05-02,78,S,0,0.0,52.0,0 644 | 2021-05-03,78,S,0,0.0,54.0,0 645 | 2021-05-04,78,S,0,0.0,52.0,0 646 | 2021-05-05,78,S,0,0.0,52.0,0 647 | 2021-05-06,78,S,0,0.0,53.0,0 648 | 2021-05-07,78,S,0,0.0,51.0,0 649 | 2021-05-08,78,S,0,0.0,52.0,0 650 | 2021-05-09,78,S,0,0.0,53.0,0 651 | 2021-05-10,78,S,0,0.0,53.0,0 652 | 2021-05-11,78,S,0,0.0,52.0,0 653 | 2021-05-12,78,S,0,0.0,52.0,0 654 | 2021-05-13,78,S,0,0.0,53.0,0 655 | 2021-05-14,78,S,0,0.0,51.0,0 656 | 2021-05-15,78,S,0,0.0,51.0,1 657 | 2021-05-16,78,S,0,0.0,52.0,1 658 | 2021-05-17,78,S,0,0.0,53.0,1 659 | 2021-05-18,78,S,0,0.0,54.0,1 660 | 2021-05-19,78,S,0,0.0,52.0,1 661 | 2021-05-20,78,S,0,0.0,52.0,1 662 | 2021-05-21,78,S,0,0.0,52.0,1 663 | 2021-05-22,78,S,0,0.0,51.0,1 664 | 2021-05-23,78,S,0,0.0,52.0,1 665 | 2021-05-24,78,S,0,0.0,52.0,1 666 | 2021-05-25,78,S,0,0.0,53.0,1 667 | 2021-05-26,78,S,0,0.0,52.0,1 668 | 2021-05-27,78,S,0,0.0,51.0,1 669 | 2021-05-28,78,S,0,0.0,51.0,1 670 | 2021-05-29,78,S,0,0.0,51.0,1 671 | 2021-05-30,78,S,0,0.0,51.0,1 672 | 2021-05-31,78,S,0,0.0,53.0,1 673 | 2021-06-01,78,S,0,0.0,53.0,1 674 | 2021-05-01,83,S,0,0.0,53.0,0 675 | 2021-05-02,83,S,0,0.0,55.0,0 676 | 2021-05-03,83,S,0,0.0,55.0,0 677 | 2021-05-04,83,S,0,0.0,53.0,0 678 | 2021-05-05,83,S,0,0.0,53.0,0 679 | 2021-05-06,83,S,0,0.0,54.0,0 680 | 2021-05-07,83,S,0,0.0,53.0,0 681 | 2021-05-08,83,S,0,0.0,53.0,0 682 | 2021-05-09,83,S,0,0.0,54.0,0 683 | 2021-05-10,83,S,0,0.0,54.0,0 684 | 2021-05-11,83,S,0,0.0,55.0,0 685 | 2021-05-12,83,S,0,0.0,52.0,0 686 | 2021-05-13,83,S,0,0.0,53.0,0 687 | 2021-05-14,83,S,0,0.0,54.0,0 688 | 2021-05-15,83,S,0,0.0,52.0,1 689 | 2021-05-16,83,S,0,0.0,53.0,1 690 | 2021-05-17,83,S,0,0.0,55.0,1 691 | 2021-05-18,83,S,0,0.0,53.0,1 692 | 2021-05-19,83,S,0,0.0,55.0,1 693 | 2021-05-20,83,S,0,0.0,53.0,1 694 | 2021-05-21,83,S,0,0.0,54.0,1 695 | 2021-05-22,83,S,0,0.0,54.0,1 696 | 2021-05-23,83,S,0,0.0,54.0,1 697 | 2021-05-24,83,S,0,0.0,54.0,1 698 | 2021-05-25,83,S,0,0.0,53.0,1 699 | 2021-05-26,83,S,0,0.0,53.0,1 700 | 2021-05-27,83,S,0,0.0,54.0,1 701 | 2021-05-28,83,S,0,0.0,53.0,1 702 | 2021-05-29,83,S,0,0.0,52.0,1 703 | 2021-05-30,83,S,0,0.0,53.0,1 704 | 2021-05-31,83,S,0,0.0,54.0,1 705 | 2021-06-01,83,S,0,0.0,54.0,1 706 | 2021-05-01,87,S,0,0.0,50.0,0 707 | 2021-05-02,87,S,0,0.0,51.0,0 708 | 2021-05-03,87,S,0,0.0,51.0,0 709 | 2021-05-04,87,S,0,0.0,51.0,0 710 | 2021-05-05,87,S,0,0.0,50.0,0 711 | 2021-05-06,87,S,0,0.0,51.0,0 712 | 2021-05-07,87,S,0,0.0,49.0,0 713 | 2021-05-08,87,S,0,0.0,50.0,0 714 | 2021-05-09,87,S,0,0.0,51.0,0 715 | 2021-05-10,87,S,0,0.0,51.0,0 716 | 2021-05-11,87,S,0,0.0,51.0,0 717 | 2021-05-12,87,S,0,0.0,51.0,0 718 | 2021-05-13,87,S,0,0.0,49.0,0 719 | 2021-05-14,87,S,0,0.0,49.0,0 720 | 2021-05-15,87,S,0,0.0,50.0,1 721 | 2021-05-16,87,S,0,0.0,51.0,1 722 | 2021-05-17,87,S,0,0.0,52.0,1 723 | 2021-05-18,87,S,0,0.0,51.0,1 724 | 2021-05-19,87,S,0,0.0,51.0,1 725 | 2021-05-20,87,S,0,0.0,52.0,1 726 | 2021-05-21,87,S,0,0.0,51.0,1 727 | 2021-05-22,87,S,0,0.0,50.0,1 728 | 2021-05-23,87,S,0,0.0,49.0,1 729 | 2021-05-24,87,S,0,0.0,52.0,1 730 | 2021-05-25,87,S,0,0.0,51.0,1 731 | 2021-05-26,87,S,0,0.0,50.0,1 732 | 2021-05-27,87,S,0,0.0,49.0,1 733 | 2021-05-28,87,S,0,0.0,49.0,1 734 | 2021-05-29,87,S,0,0.0,50.0,1 735 | 2021-05-30,87,S,0,0.0,49.0,1 736 | 2021-05-31,87,S,0,0.0,52.0,1 737 | 2021-06-01,87,S,0,0.0,52.0,1 738 | 2021-05-01,99,S,0,0.0,48.0,0 739 | 2021-05-02,99,S,0,0.0,47.0,0 740 | 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2021-05-19,196,S,0,0.0,51.0,1 1589 | 2021-05-20,196,S,0,0.0,53.0,1 1590 | 2021-05-21,196,S,0,0.0,52.0,1 1591 | 2021-05-22,196,S,0,0.0,52.0,1 1592 | 2021-05-23,196,S,0,0.0,51.0,1 1593 | 2021-05-24,196,S,0,0.0,53.0,1 1594 | 2021-05-25,196,S,0,0.0,52.0,1 1595 | 2021-05-26,196,S,0,0.0,52.0,1 1596 | 2021-05-27,196,S,0,0.0,53.0,1 1597 | 2021-05-28,196,S,0,0.0,51.0,1 1598 | 2021-05-29,196,S,0,0.0,51.0,1 1599 | 2021-05-30,196,S,0,0.0,52.0,1 1600 | 2021-05-31,196,S,0,0.0,54.0,1 1601 | 2021-06-01,196,S,0,0.0,54.0,1 1602 | 2021-05-01,197,S,1,0.0,51.0,0 1603 | 2021-05-02,197,S,1,0.0,52.0,0 1604 | 2021-05-03,197,S,1,0.0,53.0,0 1605 | 2021-05-04,197,S,1,0.0,50.0,0 1606 | 2021-05-05,197,S,1,0.0,52.0,0 1607 | 2021-05-06,197,S,1,0.0,52.0,0 1608 | 2021-05-07,197,S,1,0.0,51.0,0 1609 | 2021-05-08,197,S,1,0.0,50.0,0 1610 | 2021-05-09,197,S,1,0.0,49.0,0 1611 | 2021-05-10,197,S,1,0.0,52.0,0 1612 | 2021-05-11,197,S,1,0.0,52.0,0 1613 | 2021-05-12,197,S,1,0.0,51.0,0 1614 | 2021-05-13,197,S,1,0.0,50.0,0 1615 | 2021-05-14,197,S,1,0.0,50.0,0 1616 | 2021-05-15,197,S,1,0.0,51.0,1 1617 | 2021-05-16,197,S,1,0.35424657427417827,50.0,1 1618 | 2021-05-17,197,S,1,0.7084931485483494,52.0,1 1619 | 2021-05-18,197,S,1,1.0627397228225277,52.0,1 1620 | 2021-05-19,197,S,1,1.4169862970966989,51.0,1 1621 | 2021-05-20,197,S,1,1.7712328713708771,54.0,1 1622 | 2021-05-21,197,S,1,1.7712328713708771,53.0,1 1623 | 2021-05-22,197,S,1,1.7712328713708771,54.0,1 1624 | 2021-05-23,197,S,1,1.7712328713708771,52.0,1 1625 | 2021-05-24,197,S,1,1.7712328713708771,54.0,1 1626 | 2021-05-25,197,S,1,1.7712328713708771,54.0,1 1627 | 2021-05-26,197,S,1,1.7712328713708771,52.0,1 1628 | 2021-05-27,197,S,1,1.7712328713708771,52.0,1 1629 | 2021-05-28,197,S,1,1.7712328713708771,53.0,1 1630 | 2021-05-29,197,S,1,1.7712328713708771,52.0,1 1631 | 2021-05-30,197,S,1,1.7712328713708771,54.0,1 1632 | 2021-05-31,197,S,1,1.7712328713708771,53.0,1 1633 | 2021-06-01,197,S,1,1.7712328713708771,55.0,1 1634 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/spend_data_rnd.csv: -------------------------------------------------------------------------------- 1 | wage,educ,exper,married,credit_score1,credit_score1_buckets,credit_limit,spend 2 | 890.0,11,16,1,533.0,600,6900.0,3144 3 | 640.0,11,7,1,467.0,400,4600.0,3054 4 | 1160.0,14,9,1,600.0,600,5100.0,3668 5 | 1180.0,15,8,1,633.0,600,6200.0,2792 6 | 920.0,16,1,1,733.0,800,5900.0,3140 7 | 900.0,12,4,1,633.0,600,5200.0,3242 8 | 970.0,11,19,1,633.0,600,6700.0,4049 9 | 1000.0,12,6,1,767.0,800,7100.0,3368 10 | 1640.0,17,7,1,633.0,600,5600.0,5159 11 | 1160.0,14,9,1,700.0,800,7400.0,4352 12 | 590.0,10,19,1,667.0,600,6600.0,2190 13 | 630.0,13,8,1,667.0,600,2900.0,3066 14 | 1010.0,14,16,1,767.0,800,8600.0,4022 15 | 1310.0,12,5,1,867.0,800,6500.0,4743 16 | 830.0,12,11,1,433.0,400,5500.0,2940 17 | 930.0,12,14,1,533.0,600,6500.0,3337 18 | 1240.0,16,11,1,367.0,400,4900.0,2487 19 | 1430.0,12,10,1,600.0,600,6400.0,2638 20 | 740.0,12,16,1,267.0,200,2900.0,2228 21 | 1120.0,16,13,0,633.0,600,5300.0,3084 22 | 1000.0,9,17,1,433.0,400,4800.0,3049 23 | 1460.0,16,11,1,500.0,400,4800.0,3427 24 | 820.0,13,14,1,700.0,800,8000.0,3420 25 | 610.0,10,20,1,667.0,600,5400.0,2573 26 | 1450.0,12,8,1,267.0,200,2200.0,2727 27 | 1910.0,11,14,1,467.0,400,5000.0,5025 28 | 840.0,12,17,1,400.0,400,5000.0,3743 29 | 770.0,15,10,0,533.0,600,6800.0,2382 30 | 1400.0,12,15,1,400.0,400,5700.0,4848 31 | 2480.0,17,8,1,500.0,400,3400.0,5425 32 | 940.0,14,6,1,800.0,800,6700.0,2736 33 | 1170.0,16,9,0,433.0,400,6200.0,3291 34 | 910.0,14,11,1,567.0,600,6600.0,3527 35 | 1440.0,16,9,1,667.0,600,6400.0,4156 36 | 1180.0,18,11,1,700.0,800,6300.0,5072 37 | 1100.0,15,8,1,633.0,600,7100.0,4068 38 | 1140.0,12,18,1,667.0,600,8100.0,4186 39 | 1820.0,16,10,1,800.0,800,6000.0,3858 40 | 1030.0,12,11,1,533.0,600,8000.0,3776 41 | 1410.0,12,13,1,933.0,1000,8800.0,5379 42 | 1020.0,12,16,1,500.0,400,6100.0,3295 43 | 780.0,11,11,1,867.0,800,6800.0,3210 44 | 920.0,16,10,1,733.0,800,5300.0,2951 45 | 820.0,13,17,0,600.0,600,5200.0,2976 46 | 910.0,12,4,1,267.0,200,1100.0,3067 47 | 740.0,13,10,1,500.0,400,5800.0,3269 48 | 1100.0,14,9,1,700.0,800,5100.0,3488 49 | 1540.0,12,19,1,500.0,400,3500.0,4253 50 | 720.0,16,7,1,800.0,800,4400.0,3071 51 | 2230.0,18,10,1,633.0,600,5300.0,4084 52 | 1020.0,12,13,1,733.0,800,7100.0,3310 53 | 1390.0,11,19,1,667.0,600,5400.0,4434 54 | 370.0,12,9,1,567.0,600,3900.0,2695 55 | 610.0,12,6,1,467.0,400,5100.0,2603 56 | 480.0,12,15,1,800.0,800,7000.0,2399 57 | 1210.0,12,13,1,567.0,600,7700.0,4439 58 | 1570.0,17,7,1,733.0,800,5100.0,3690 59 | 440.0,17,8,0,567.0,600,4400.0,1525 60 | 720.0,18,4,1,333.0,400,2600.0,2092 61 | 1190.0,16,13,1,633.0,600,6700.0,2549 62 | 1290.0,12,5,1,733.0,800,5500.0,3990 63 | 540.0,12,4,1,500.0,400,3200.0,2944 64 | 1200.0,13,21,1,567.0,600,4300.0,3746 65 | 1000.0,12,17,1,967.0,1000,7700.0,4347 66 | 950.0,18,1,1,967.0,1000,6300.0,2994 67 | 2790.0,18,6,1,600.0,600,5400.0,5310 68 | 790.0,15,16,1,700.0,800,5500.0,1968 69 | 1230.0,14,18,1,700.0,800,5600.0,3237 70 | 1340.0,11,19,1,1033.0,1000,6600.0,4367 71 | 1090.0,16,8,1,667.0,600,2200.0,2716 72 | 920.0,16,10,1,433.0,400,7100.0,2733 73 | 690.0,13,10,1,933.0,1000,7700.0,4254 74 | 1230.0,18,13,1,867.0,800,4900.0,3577 75 | 790.0,11,7,1,433.0,400,5500.0,3188 76 | 730.0,12,14,1,800.0,800,5900.0,3113 77 | 610.0,12,11,0,433.0,400,3800.0,1602 78 | 770.0,12,4,0,367.0,400,6500.0,2405 79 | 1040.0,12,17,0,467.0,400,5500.0,3623 80 | 1060.0,12,14,1,300.0,400,6400.0,3034 81 | 1010.0,12,10,1,700.0,800,4300.0,3924 82 | 570.0,12,7,0,767.0,800,6800.0,2612 83 | 710.0,13,9,1,733.0,800,4700.0,3637 84 | 870.0,12,11,1,467.0,400,3600.0,1972 85 | 950.0,10,13,1,567.0,600,6100.0,3125 86 | 810.0,12,6,1,633.0,600,7600.0,2606 87 | 1260.0,12,18,1,800.0,800,4200.0,3579 88 | 1010.0,16,7,1,867.0,800,4400.0,3065 89 | 680.0,12,17,1,800.0,800,6900.0,3651 90 | 930.0,13,12,1,567.0,600,5600.0,3753 91 | 760.0,12,7,0,600.0,600,6500.0,3388 92 | 1290.0,15,15,1,500.0,400,6400.0,3902 93 | 1240.0,9,18,1,500.0,400,5300.0,2785 94 | 980.0,13,15,1,900.0,800,7100.0,3872 95 | 440.0,12,16,1,500.0,400,5300.0,1935 96 | 290.0,10,11,1,667.0,600,4100.0,2585 97 | 1240.0,18,10,1,633.0,600,6000.0,2569 98 | 1100.0,10,15,1,467.0,400,1300.0,3032 99 | 1810.0,15,8,1,167.0,200,3300.0,3702 100 | 1930.0,16,16,1,467.0,400,4300.0,4194 101 | 930.0,16,9,0,567.0,600,5200.0,4121 102 | 1220.0,16,9,1,467.0,400,5200.0,4685 103 | 700.0,11,17,1,367.0,400,6500.0,3370 104 | 710.0,15,12,1,667.0,600,5700.0,3199 105 | 1040.0,12,4,1,400.0,400,4700.0,3305 106 | 1210.0,12,14,1,633.0,600,6800.0,2951 107 | 1440.0,16,15,1,633.0,600,5700.0,4077 108 | 2660.0,12,21,1,867.0,800,4600.0,5305 109 | 880.0,16,9,0,667.0,600,7100.0,3120 110 | 1490.0,17,7,1,167.0,200,3100.0,3922 111 | 750.0,12,13,1,167.0,200,1100.0,2517 112 | 620.0,12,11,1,800.0,800,6000.0,3376 113 | 1240.0,12,11,1,700.0,800,5300.0,3277 114 | 820.0,13,1,1,633.0,600,5100.0,4147 115 | 770.0,10,23,1,667.0,600,6300.0,2666 116 | 610.0,11,7,1,833.0,800,6800.0,3327 117 | 870.0,18,7,0,533.0,600,5600.0,3666 118 | 1000.0,13,4,1,233.0,200,3400.0,2680 119 | 1900.0,16,9,1,433.0,400,4300.0,4396 120 | 1150.0,12,11,1,700.0,800,5400.0,3495 121 | 500.0,12,10,0,567.0,600,5500.0,2249 122 | 1970.0,18,13,1,533.0,600,5500.0,4653 123 | 1060.0,10,19,1,900.0,800,7100.0,2071 124 | 1370.0,12,16,1,400.0,400,5100.0,3136 125 | 780.0,13,16,1,667.0,600,4100.0,3149 126 | 1280.0,16,10,1,467.0,400,5600.0,2827 127 | 710.0,18,6,1,333.0,400,5700.0,1962 128 | 1030.0,12,14,1,800.0,800,7800.0,4179 129 | 600.0,12,12,1,433.0,400,6500.0,3858 130 | 750.0,14,7,1,600.0,600,6900.0,3957 131 | 830.0,12,16,1,933.0,1000,7000.0,3910 132 | 900.0,16,10,1,767.0,800,5300.0,3181 133 | 2410.0,14,1,1,633.0,600,5600.0,4262 134 | 840.0,12,11,1,467.0,400,6300.0,3149 135 | 1050.0,16,9,1,733.0,800,6200.0,2610 136 | 1510.0,15,6,1,600.0,600,3600.0,3837 137 | 1330.0,16,11,1,333.0,400,4300.0,3346 138 | 1190.0,14,13,1,367.0,400,6400.0,4365 139 | 880.0,12,13,1,500.0,400,4000.0,2089 140 | 1030.0,10,14,1,400.0,400,4400.0,3613 141 | 830.0,17,7,1,500.0,400,6500.0,2535 142 | 1260.0,14,18,1,367.0,400,5800.0,3854 143 | 1070.0,12,12,1,500.0,400,2000.0,3077 144 | 780.0,11,10,1,67.0,0,300.0,2732 145 | 1460.0,16,11,1,767.0,800,5600.0,3102 146 | 740.0,17,12,1,433.0,400,9100.0,3896 147 | 370.0,16,7,1,533.0,600,6500.0,2468 148 | 1370.0,12,19,1,567.0,600,4700.0,2964 149 | 1080.0,18,8,1,833.0,800,7700.0,3746 150 | 820.0,12,11,1,400.0,400,3600.0,2933 151 | 730.0,14,9,1,767.0,800,5600.0,3236 152 | 1470.0,13,8,1,800.0,800,6000.0,3875 153 | 840.0,11,15,1,567.0,600,6800.0,3700 154 | 590.0,12,16,1,667.0,600,8100.0,2005 155 | 800.0,17,8,1,1067.0,1000,6800.0,4855 156 | 990.0,17,4,1,633.0,600,5400.0,2384 157 | 1820.0,17,10,1,633.0,600,5900.0,4161 158 | 780.0,12,17,1,567.0,600,4600.0,3315 159 | 380.0,15,6,0,500.0,400,3300.0,1367 160 | 1040.0,12,15,1,600.0,600,4200.0,4852 161 | 2190.0,16,12,1,767.0,800,5600.0,5484 162 | 880.0,12,11,1,467.0,400,4600.0,3442 163 | 790.0,12,13,1,900.0,800,3400.0,2417 164 | 760.0,16,8,0,733.0,800,4600.0,1552 165 | 660.0,13,8,1,500.0,400,5500.0,2186 166 | 560.0,10,11,0,700.0,800,4900.0,1738 167 | 1460.0,15,15,1,467.0,400,3600.0,3290 168 | 1190.0,11,15,1,733.0,800,7700.0,3652 169 | 1270.0,13,11,1,567.0,600,6700.0,3769 170 | 1490.0,16,9,1,467.0,400,5400.0,4257 171 | 650.0,12,12,1,967.0,1000,7100.0,3378 172 | 900.0,12,10,1,533.0,600,4900.0,2931 173 | 940.0,12,15,1,800.0,800,5500.0,3358 174 | 1420.0,12,17,1,800.0,800,8200.0,4830 175 | 560.0,15,8,1,900.0,800,6000.0,3184 176 | 550.0,13,20,1,533.0,600,5900.0,2691 177 | 1500.0,16,11,1,667.0,600,5300.0,4718 178 | 430.0,15,6,0,767.0,800,5700.0,2239 179 | 1980.0,11,22,1,567.0,600,7600.0,5050 180 | 1580.0,17,1,1,700.0,800,6200.0,2560 181 | 1030.0,16,9,1,867.0,800,7000.0,2770 182 | 1130.0,12,17,1,433.0,400,3600.0,3171 183 | 1180.0,15,13,1,767.0,800,6500.0,2358 184 | 620.0,12,18,1,300.0,400,2300.0,962 185 | 980.0,12,17,0,467.0,400,6100.0,3117 186 | 710.0,12,10,0,667.0,600,4800.0,2906 187 | 840.0,10,23,1,700.0,800,8100.0,4290 188 | 2030.0,18,13,1,933.0,1000,7400.0,5381 189 | 930.0,16,5,1,600.0,600,7400.0,2723 190 | 930.0,18,7,1,500.0,400,2100.0,2968 191 | 1090.0,15,8,1,433.0,400,2600.0,2543 192 | 1100.0,12,13,1,467.0,400,5700.0,3957 193 | 400.0,12,4,1,667.0,600,5700.0,2304 194 | 1030.0,11,16,1,367.0,400,3800.0,3431 195 | 740.0,15,14,1,733.0,800,6900.0,2764 196 | 710.0,12,20,1,533.0,600,7500.0,4044 197 | 980.0,12,16,1,867.0,800,7100.0,3182 198 | 1390.0,17,1,1,633.0,600,4200.0,3666 199 | 1330.0,12,13,1,833.0,800,7100.0,2975 200 | 1690.0,12,5,1,933.0,1000,7300.0,3941 201 | 490.0,10,12,1,533.0,600,6400.0,2806 202 | 990.0,12,6,0,600.0,600,5100.0,2360 203 | 750.0,12,8,0,433.0,400,2500.0,2133 204 | 670.0,12,18,1,767.0,800,6800.0,2971 205 | 860.0,11,16,1,700.0,800,5500.0,3082 206 | 670.0,12,13,1,533.0,600,5100.0,3341 207 | 1140.0,12,16,1,200.0,200,4200.0,3070 208 | 1240.0,13,20,1,533.0,600,7900.0,3266 209 | 630.0,11,17,1,667.0,600,4900.0,3407 210 | 960.0,11,19,1,500.0,400,2000.0,2913 211 | 620.0,13,8,1,833.0,800,7100.0,3156 212 | 1840.0,12,10,0,300.0,400,3200.0,4647 213 | 1330.0,18,14,1,700.0,800,6000.0,3728 214 | 1830.0,16,14,1,700.0,800,6200.0,4717 215 | 2340.0,13,8,1,433.0,400,6800.0,5253 216 | 680.0,16,8,0,1000.0,1000,7600.0,2778 217 | 1210.0,12,18,1,267.0,200,3000.0,2561 218 | 1630.0,18,10,1,500.0,400,2700.0,3515 219 | 1190.0,16,9,0,467.0,400,3200.0,2131 220 | 770.0,12,13,0,500.0,400,5200.0,1829 221 | 1140.0,12,16,1,733.0,800,5600.0,2585 222 | 1350.0,13,10,1,633.0,600,4300.0,3590 223 | 910.0,13,15,1,633.0,600,7400.0,3140 224 | 1200.0,14,9,1,533.0,600,3200.0,2823 225 | 1500.0,16,10,1,500.0,400,4100.0,2468 226 | 1560.0,16,9,0,367.0,400,2500.0,3511 227 | 1330.0,12,10,1,633.0,600,6200.0,4867 228 | 1090.0,12,11,1,600.0,600,3200.0,2821 229 | 1400.0,17,13,0,333.0,400,2000.0,2300 230 | 1210.0,13,13,1,467.0,400,4300.0,3813 231 | 1320.0,18,7,1,600.0,600,4600.0,3201 232 | 890.0,12,16,1,600.0,600,6500.0,2782 233 | 430.0,12,14,1,367.0,400,3500.0,1890 234 | 910.0,12,16,1,1000.0,1000,5800.0,4005 235 | 1130.0,12,13,1,467.0,400,3900.0,2970 236 | 1090.0,12,16,1,733.0,800,7400.0,2687 237 | 1880.0,16,8,1,467.0,400,3600.0,4136 238 | 1180.0,12,4,1,433.0,400,2900.0,3138 239 | 1040.0,16,7,1,433.0,400,3900.0,3656 240 | 780.0,12,17,1,533.0,600,6600.0,3133 241 | 1520.0,13,3,1,767.0,800,4100.0,3687 242 | 730.0,12,17,1,567.0,600,6600.0,3065 243 | 830.0,11,17,1,467.0,400,5200.0,3495 244 | 530.0,11,16,1,400.0,400,6000.0,3721 245 | 910.0,12,17,1,767.0,800,5800.0,3694 246 | 1150.0,12,11,1,600.0,600,7900.0,3783 247 | 910.0,16,8,0,667.0,600,5700.0,1834 248 | 960.0,13,14,1,733.0,800,3900.0,3123 249 | 900.0,12,16,1,567.0,600,5200.0,3094 250 | 1090.0,11,9,1,567.0,600,2600.0,3574 251 | 860.0,12,20,1,833.0,800,7000.0,3139 252 | 930.0,14,9,1,633.0,600,6400.0,4424 253 | 690.0,9,15,1,500.0,400,4800.0,2708 254 | 400.0,12,7,1,333.0,400,3800.0,2378 255 | 1650.0,18,7,1,533.0,600,4500.0,4352 256 | 640.0,12,8,1,567.0,600,7400.0,3755 257 | 1330.0,10,13,1,533.0,600,3600.0,3464 258 | 850.0,14,9,1,800.0,800,4400.0,2409 259 | 870.0,9,19,1,733.0,800,7600.0,3258 260 | 950.0,18,8,1,600.0,600,6800.0,4708 261 | 1270.0,12,16,1,700.0,800,7600.0,4024 262 | 580.0,10,20,1,467.0,400,6500.0,3166 263 | 1750.0,18,8,1,533.0,600,4600.0,4684 264 | 570.0,12,11,1,400.0,400,4500.0,2714 265 | 600.0,13,10,1,733.0,800,5600.0,2844 266 | 680.0,12,14,1,400.0,400,6300.0,4093 267 | 1290.0,14,16,1,633.0,600,6200.0,3716 268 | 1470.0,16,7,1,700.0,800,5500.0,4097 269 | 1170.0,15,8,1,867.0,800,7500.0,4338 270 | 890.0,18,8,1,400.0,400,6900.0,3697 271 | 1450.0,18,12,1,867.0,800,5300.0,4251 272 | 1110.0,13,11,1,633.0,600,4800.0,2707 273 | 1050.0,13,11,1,733.0,800,6900.0,4382 274 | 910.0,12,15,1,400.0,400,5700.0,3272 275 | 620.0,16,8,1,700.0,800,5300.0,3720 276 | 990.0,16,7,1,933.0,1000,7700.0,3544 277 | 350.0,12,9,1,667.0,600,5900.0,2422 278 | 1210.0,12,17,1,500.0,400,5900.0,2773 279 | 1400.0,15,15,1,567.0,600,6300.0,3709 280 | 610.0,9,15,1,733.0,800,6600.0,2787 281 | 810.0,12,16,1,633.0,600,6600.0,3418 282 | 530.0,12,4,1,367.0,400,5000.0,2559 283 | 650.0,18,6,1,333.0,400,5200.0,3893 284 | 700.0,12,6,1,667.0,600,4700.0,3109 285 | 1300.0,12,16,1,800.0,800,6700.0,3296 286 | 740.0,17,12,1,533.0,600,4500.0,3418 287 | 1000.0,18,8,1,667.0,600,2600.0,3625 288 | 1000.0,12,15,1,333.0,400,4900.0,4404 289 | 1450.0,12,12,1,900.0,800,5900.0,3483 290 | 1460.0,13,3,1,600.0,600,5000.0,3895 291 | 1130.0,12,20,1,833.0,800,5400.0,4242 292 | 530.0,12,15,1,433.0,400,5200.0,2034 293 | 1460.0,16,7,1,200.0,200,3300.0,3171 294 | 950.0,12,17,1,433.0,400,2400.0,2612 295 | 1350.0,13,13,1,600.0,600,6600.0,4239 296 | 830.0,12,15,1,600.0,600,5200.0,3357 297 | 810.0,14,7,1,733.0,800,2700.0,2150 298 | 1430.0,13,13,1,500.0,400,3300.0,4212 299 | 1000.0,17,8,1,633.0,600,6500.0,2925 300 | 830.0,12,17,1,467.0,400,4200.0,2642 301 | 790.0,16,10,0,500.0,400,4900.0,2846 302 | 730.0,10,14,1,400.0,400,4000.0,2987 303 | 870.0,12,16,1,467.0,400,5800.0,3937 304 | 1200.0,12,16,1,633.0,600,4400.0,3772 305 | 900.0,16,9,0,567.0,600,4100.0,2514 306 | 1000.0,16,11,1,467.0,400,6200.0,2209 307 | 950.0,14,8,1,500.0,400,2600.0,1988 308 | 1360.0,18,14,1,633.0,600,5000.0,3422 309 | 1390.0,12,11,0,367.0,400,4600.0,3371 310 | 750.0,12,7,1,500.0,400,6300.0,3900 311 | 1520.0,16,6,1,633.0,600,5900.0,3667 312 | 1380.0,14,10,1,733.0,800,4800.0,3787 313 | 700.0,12,12,1,533.0,600,4300.0,3423 314 | 1770.0,18,12,1,600.0,600,6600.0,3765 315 | 910.0,14,8,1,233.0,200,900.0,1970 316 | 840.0,13,14,1,500.0,400,6200.0,4255 317 | 920.0,10,13,1,567.0,600,6400.0,1971 318 | 700.0,12,12,1,600.0,600,5600.0,3493 319 | 1380.0,16,6,0,300.0,400,5200.0,2656 320 | 1670.0,12,16,1,733.0,800,6200.0,4622 321 | 1580.0,12,17,1,300.0,400,6400.0,3711 322 | 1080.0,12,16,1,867.0,800,6000.0,3633 323 | 930.0,12,6,0,667.0,600,5100.0,2445 324 | 1900.0,16,9,1,800.0,800,5900.0,4841 325 | 1540.0,17,8,1,533.0,600,5000.0,3759 326 | 890.0,15,1,1,667.0,600,6400.0,3097 327 | 1560.0,16,8,1,333.0,400,1600.0,3469 328 | 1020.0,13,11,1,667.0,600,2700.0,3022 329 | 1380.0,16,4,1,467.0,400,1500.0,3674 330 | 1160.0,14,6,1,433.0,400,4800.0,3189 331 | 1800.0,18,12,1,500.0,400,3600.0,3520 332 | 790.0,13,9,1,567.0,600,3400.0,2606 333 | 1270.0,13,10,1,367.0,400,5500.0,3432 334 | 650.0,17,8,0,467.0,400,3700.0,2853 335 | 1000.0,11,15,1,333.0,400,5900.0,4202 336 | 500.0,12,9,0,600.0,600,4900.0,1594 337 | 1600.0,12,15,1,400.0,400,2100.0,2725 338 | 1120.0,12,18,1,500.0,400,7300.0,3026 339 | 1650.0,16,12,1,867.0,800,5100.0,5121 340 | 1860.0,12,11,1,833.0,800,3900.0,3145 341 | 1250.0,12,17,1,833.0,800,6200.0,3994 342 | 980.0,10,15,1,700.0,800,6900.0,3243 343 | 890.0,11,16,1,533.0,600,4600.0,3032 344 | 1500.0,16,11,1,767.0,800,6900.0,4152 345 | 840.0,12,16,1,733.0,800,7500.0,3603 346 | 1470.0,12,19,1,567.0,600,5200.0,3835 347 | 600.0,14,9,1,700.0,800,6700.0,3330 348 | 440.0,12,21,1,567.0,600,7000.0,3337 349 | 1620.0,17,7,1,433.0,400,5200.0,4299 350 | 620.0,11,13,0,700.0,800,5700.0,1543 351 | 2610.0,12,21,1,800.0,800,7000.0,4828 352 | 730.0,12,16,1,667.0,600,7000.0,2715 353 | 710.0,13,9,1,700.0,800,5400.0,2972 354 | 1120.0,18,8,1,733.0,800,5800.0,4055 355 | 640.0,12,11,1,767.0,800,8200.0,3103 356 | 1090.0,12,17,1,667.0,600,5000.0,3266 357 | 870.0,12,9,1,867.0,800,5300.0,3759 358 | 960.0,12,17,1,600.0,600,4300.0,3258 359 | 2180.0,16,16,1,600.0,600,3200.0,3814 360 | 1040.0,16,7,1,400.0,400,4400.0,4380 361 | 1120.0,12,11,1,733.0,800,5400.0,3334 362 | 1030.0,12,17,1,500.0,400,4800.0,4396 363 | 840.0,11,16,1,567.0,600,6200.0,3063 364 | 1170.0,16,11,1,567.0,600,5500.0,2972 365 | 1000.0,12,15,1,567.0,600,5100.0,2904 366 | 840.0,12,11,1,600.0,600,8000.0,2584 367 | 440.0,12,16,1,467.0,400,4600.0,2531 368 | 1040.0,16,8,1,400.0,400,4000.0,4019 369 | 890.0,14,1,1,500.0,400,3700.0,3122 370 | 1720.0,15,9,0,667.0,600,7400.0,3150 371 | 1390.0,14,4,1,567.0,600,7000.0,3101 372 | 700.0,14,5,1,767.0,800,4800.0,4700 373 | 1190.0,18,11,1,733.0,800,5700.0,4213 374 | 640.0,9,15,1,867.0,800,5900.0,1855 375 | 550.0,12,5,1,400.0,400,4500.0,2881 376 | 460.0,10,7,1,467.0,400,3400.0,1687 377 | 580.0,16,8,1,933.0,1000,6300.0,3094 378 | 1600.0,12,16,1,400.0,400,5700.0,3711 379 | 710.0,18,8,0,800.0,800,7900.0,2708 380 | 490.0,12,13,1,67.0,0,1800.0,2875 381 | 1160.0,12,17,1,533.0,600,5300.0,3208 382 | 990.0,18,11,1,567.0,600,4800.0,3176 383 | 610.0,12,6,1,600.0,600,4000.0,2727 384 | 1050.0,16,6,1,333.0,400,4000.0,2565 385 | 860.0,10,17,1,767.0,800,6900.0,3988 386 | 440.0,12,12,1,767.0,800,4100.0,2380 387 | 860.0,12,13,1,800.0,800,6200.0,3613 388 | 1600.0,16,8,1,500.0,400,6800.0,4209 389 | 2200.0,13,8,1,533.0,600,1300.0,3825 390 | 1130.0,13,11,1,500.0,400,4700.0,3287 391 | 1550.0,16,15,1,533.0,600,6500.0,3160 392 | 870.0,12,15,1,900.0,800,3300.0,2949 393 | 1150.0,12,4,1,800.0,800,3800.0,3331 394 | 1530.0,17,15,1,600.0,600,5000.0,4187 395 | 550.0,16,6,1,567.0,600,4200.0,2325 396 | 750.0,12,7,1,733.0,800,6600.0,2930 397 | 1170.0,11,17,0,467.0,400,6200.0,2264 398 | 1060.0,14,16,1,333.0,400,6600.0,2788 399 | 1230.0,13,16,1,633.0,600,3700.0,4485 400 | 1130.0,11,15,1,700.0,800,5200.0,2801 401 | 790.0,16,10,1,567.0,600,7500.0,1441 402 | 960.0,15,4,0,567.0,600,2800.0,3505 403 | 1790.0,14,15,1,733.0,800,6700.0,4155 404 | 990.0,12,11,1,700.0,800,4600.0,3528 405 | 1430.0,16,8,1,467.0,400,3300.0,4185 406 | 920.0,12,16,1,467.0,400,3900.0,2120 407 | 1710.0,15,14,1,700.0,800,5700.0,4228 408 | 1500.0,17,7,1,733.0,800,5000.0,3801 409 | 1270.0,13,13,1,500.0,400,7300.0,3515 410 | 670.0,15,8,1,600.0,600,6300.0,3570 411 | 1490.0,18,14,1,933.0,1000,7700.0,3617 412 | 1880.0,16,9,1,433.0,400,3000.0,3594 413 | 1450.0,16,14,1,567.0,600,5400.0,4388 414 | 660.0,12,16,1,733.0,800,6000.0,2031 415 | 1370.0,12,17,1,733.0,800,6500.0,2865 416 | 510.0,12,9,1,533.0,600,7000.0,2779 417 | 1350.0,13,11,1,433.0,400,4400.0,3578 418 | 940.0,12,18,1,833.0,800,7700.0,3618 419 | 1290.0,16,11,1,500.0,400,6200.0,4009 420 | 840.0,13,9,1,833.0,800,5700.0,3086 421 | 1230.0,12,12,1,767.0,800,5600.0,3513 422 | 800.0,14,6,0,733.0,800,5100.0,2752 423 | 1080.0,16,6,1,733.0,800,5000.0,3665 424 | 860.0,10,17,1,600.0,600,5100.0,2767 425 | 1200.0,12,14,1,433.0,400,4800.0,2935 426 | 750.0,10,23,1,333.0,400,6100.0,2918 427 | 720.0,12,18,0,800.0,800,6600.0,3367 428 | 1160.0,12,4,1,367.0,400,3000.0,2373 429 | 700.0,10,13,0,700.0,800,5700.0,1947 430 | 700.0,13,4,1,600.0,600,7900.0,3619 431 | 760.0,14,5,1,733.0,800,4100.0,2514 432 | 1070.0,11,16,1,567.0,600,6900.0,4428 433 | 2300.0,15,9,1,600.0,600,6300.0,5450 434 | 570.0,10,20,1,600.0,600,3800.0,3320 435 | 1190.0,16,13,0,667.0,600,5700.0,2866 436 | 1120.0,12,19,1,367.0,400,5600.0,3604 437 | 1550.0,12,19,1,800.0,800,7500.0,4414 438 | 1140.0,12,11,1,633.0,600,7200.0,3543 439 | 1380.0,16,9,1,867.0,800,6700.0,3877 440 | 1680.0,14,16,1,367.0,400,5600.0,3223 441 | 880.0,12,14,1,933.0,1000,6500.0,3561 442 | 1200.0,13,10,1,600.0,600,3100.0,2545 443 | 1300.0,12,18,1,567.0,600,2900.0,3754 444 | 1090.0,12,17,1,467.0,400,2900.0,3482 445 | 1550.0,18,14,1,500.0,400,8100.0,4087 446 | 1410.0,16,14,1,400.0,400,5100.0,3956 447 | 1430.0,16,10,0,633.0,600,6600.0,3057 448 | 430.0,10,7,1,500.0,400,1300.0,2238 449 | 1250.0,11,12,1,467.0,400,6300.0,3535 450 | 1040.0,16,9,1,667.0,600,3000.0,2603 451 | 1090.0,16,13,0,600.0,600,7700.0,2652 452 | 750.0,13,9,1,267.0,200,2600.0,2712 453 | 440.0,10,12,1,633.0,600,4500.0,2092 454 | 1060.0,16,12,1,967.0,1000,5700.0,3843 455 | 760.0,12,16,1,700.0,800,4500.0,3050 456 | 1910.0,18,12,1,833.0,800,6600.0,4285 457 | 660.0,10,12,1,733.0,800,7500.0,2944 458 | 560.0,15,8,1,667.0,600,3100.0,4095 459 | 630.0,13,10,0,600.0,600,5300.0,3243 460 | 930.0,16,8,0,533.0,600,5100.0,3199 461 | 1100.0,13,11,1,233.0,200,5100.0,4317 462 | 1060.0,16,4,1,567.0,600,5200.0,3592 463 | 1580.0,12,14,1,433.0,400,6500.0,3998 464 | 680.0,12,11,1,733.0,800,6200.0,3050 465 | 1100.0,12,16,1,533.0,600,8300.0,5063 466 | 680.0,13,8,0,667.0,600,6100.0,2425 467 | 780.0,12,4,1,767.0,800,7700.0,3312 468 | 1680.0,17,7,1,567.0,600,5100.0,3251 469 | 570.0,16,8,1,300.0,400,5200.0,2735 470 | 710.0,10,20,1,567.0,600,2900.0,2876 471 | 990.0,9,17,1,567.0,600,5100.0,4377 472 | 510.0,14,10,1,800.0,800,6400.0,4897 473 | 840.0,16,10,1,433.0,400,3100.0,3025 474 | 820.0,13,10,1,700.0,800,6400.0,3295 475 | 420.0,14,4,1,333.0,400,3500.0,1873 476 | 940.0,16,10,0,733.0,800,4400.0,1335 477 | 1440.0,16,8,1,900.0,800,5600.0,4703 478 | 1020.0,16,11,1,600.0,600,6400.0,4046 479 | 1180.0,12,16,1,533.0,600,6700.0,3168 480 | 770.0,12,13,1,567.0,600,7600.0,2625 481 | 960.0,12,16,1,533.0,600,6800.0,3755 482 | 950.0,12,19,1,767.0,800,7000.0,3212 483 | 1070.0,13,13,1,867.0,800,4700.0,2614 484 | 1390.0,16,10,1,467.0,400,4900.0,4336 485 | 820.0,18,11,1,433.0,400,2200.0,2335 486 | 790.0,12,17,1,533.0,600,6000.0,3363 487 | 1410.0,15,6,1,400.0,400,3200.0,3785 488 | 1650.0,12,15,1,633.0,600,2500.0,3019 489 | 880.0,10,21,1,567.0,600,4100.0,3247 490 | 1040.0,12,16,1,533.0,600,6200.0,2664 491 | 1190.0,12,17,1,567.0,600,3800.0,3768 492 | 670.0,12,18,0,500.0,400,3600.0,2438 493 | 1850.0,18,12,1,400.0,400,3800.0,4805 494 | 840.0,12,4,1,833.0,800,4200.0,3084 495 | 970.0,12,19,1,633.0,600,4500.0,2926 496 | 1460.0,18,12,1,533.0,600,4400.0,3214 497 | 1330.0,18,7,1,633.0,600,4900.0,2694 498 | 570.0,12,12,0,700.0,800,6600.0,3549 499 | 840.0,12,13,1,667.0,600,6300.0,2163 500 | 1060.0,14,16,1,433.0,400,3400.0,3802 501 | 1390.0,17,9,1,667.0,600,4700.0,3853 502 | -------------------------------------------------------------------------------- /실무로 통하는 인과추론 with 파이썬/data/xmas_sales.csv: -------------------------------------------------------------------------------- 1 | store,weeks_to_xmas,avg_week_sales,is_on_sale,weekly_amount_sold 2 | 1,3,12.98,1,219.6 3 | 1,2,12.98,1,184.7 4 | 1,1,12.98,1,145.75 5 | 1,0,12.98,0,102.45 6 | 2,3,19.92,0,103.22 7 | 2,2,19.92,0,53.73 8 | 2,1,19.92,0,13.84 9 | 2,0,19.92,0,0.0 10 | 3,3,18.54,0,97.02 11 | 3,2,18.54,0,54.72 12 | 3,1,18.54,0,24.6 13 | 3,0,18.54,0,0.0 14 | 4,3,20.52,1,115.38 15 | 4,2,20.52,0,53.7 16 | 4,1,20.52,1,59.95 17 | 4,0,20.52,0,0.0 18 | 5,3,17.27,0,162.6 19 | 5,2,17.27,0,128.74 20 | 5,1,17.27,0,99.84 21 | 5,0,17.27,0,35.63 22 | 6,3,20.07,1,55.79 23 | 6,2,20.07,0,0.0 24 | 6,1,20.07,0,0.0 25 | 6,0,20.07,0,0.0 26 | 7,3,28.18,1,334.23 27 | 7,2,28.18,0,220.62 28 | 7,1,28.18,1,203.68 29 | 7,0,28.18,1,153.11 30 | 8,3,20.55,0,96.5 31 | 8,2,20.55,0,54.1 32 | 8,1,20.55,0,14.76 33 | 8,0,20.55,0,0.0 34 | 9,3,22.9,1,218.92 35 | 9,2,22.9,0,126.14 36 | 9,1,22.9,1,131.76 37 | 9,0,22.9,0,39.04 38 | 10,3,16.49,0,108.97 39 | 10,2,16.49,0,46.93 40 | 10,1,16.49,1,77.35 41 | 10,0,16.49,0,1.73 42 | 11,3,20.48,1,250.99 43 | 11,2,20.48,1,206.36 44 | 11,1,20.48,0,126.57 45 | 11,0,20.48,0,96.85 46 | 12,3,25.0,1,130.5 47 | 12,2,25.0,1,39.39 48 | 12,1,25.0,0,0.0 49 | 12,0,25.0,0,0.0 50 | 13,3,20.24,0,122.01 51 | 13,2,20.24,1,124.66 52 | 13,1,20.24,0,32.28 53 | 13,0,20.24,0,0.0 54 | 14,3,28.55,1,220.8 55 | 14,2,28.55,1,178.96 56 | 14,1,28.55,1,106.75 57 | 14,0,28.55,1,63.78 58 | 15,3,26.05,1,265.06 59 | 15,2,26.05,1,234.25 60 | 15,1,26.05,1,172.04 61 | 15,0,26.05,1,122.44 62 | 16,3,17.11,1,119.88 63 | 16,2,17.11,0,6.72 64 | 16,1,17.11,0,0.0 65 | 16,0,17.11,0,0.0 66 | 17,3,27.77,1,245.78 67 | 17,2,27.77,1,167.03 68 | 17,1,27.77,1,120.15 69 | 17,0,27.77,1,71.28 70 | 18,3,16.3,1,111.93 71 | 18,2,16.3,0,28.43 72 | 18,1,16.3,1,30.74 73 | 18,0,16.3,0,0.0 74 | 19,3,18.57,0,124.0 75 | 19,2,18.57,1,133.58 76 | 19,1,18.57,0,54.02 77 | 19,0,18.57,0,27.26 78 | 20,3,21.41,0,167.15 79 | 20,2,21.41,1,185.38 80 | 20,1,21.41,0,83.39 81 | 20,0,21.41,0,49.14 82 | 21,3,22.86,1,134.0 83 | 21,2,22.86,1,113.93 84 | 21,1,22.86,0,8.25 85 | 21,0,22.86,1,15.02 86 | 22,3,18.14,0,187.41 87 | 22,2,18.14,0,138.41 88 | 22,1,18.14,0,109.2 89 | 22,0,18.14,1,118.63 90 | 23,3,17.84,0,144.92 91 | 23,2,17.84,1,185.79 92 | 23,1,17.84,1,134.51 93 | 23,0,17.84,0,32.47 94 | 24,3,16.6,1,180.6 95 | 24,2,16.6,0,111.54 96 | 24,1,16.6,0,75.5 97 | 24,0,16.6,1,67.85 98 | 25,3,11.35,0,117.02 99 | 25,2,11.35,1,152.79 100 | 25,1,11.35,1,126.55 101 | 25,0,11.35,0,50.12 102 | 26,3,13.05,1,78.59 103 | 26,2,13.05,0,29.4 104 | 26,1,13.05,0,0.0 105 | 26,0,13.05,0,0.0 106 | 27,3,26.07,0,169.61 107 | 27,2,26.07,1,177.24 108 | 27,1,26.07,0,69.33 109 | 27,0,26.07,1,48.86 110 | 28,3,14.98,0,62.98 111 | 28,2,14.98,1,69.35 112 | 28,1,14.98,1,20.33 113 | 28,0,14.98,0,0.0 114 | 29,3,24.5,1,193.36 115 | 29,2,24.5,1,158.35 116 | 29,1,24.5,1,107.57 117 | 29,0,24.5,1,48.18 118 | 30,3,13.82,1,168.43 119 | 30,2,13.82,0,87.47 120 | 30,1,13.82,0,71.87 121 | 30,0,13.82,0,36.82 122 | 31,3,18.7,1,128.14 123 | 31,2,18.7,0,41.41 124 | 31,1,18.7,0,0.0 125 | 31,0,18.7,1,13.4 126 | 32,3,26.93,1,222.3 127 | 32,2,26.93,1,147.26 128 | 32,1,26.93,0,54.1 129 | 32,0,26.93,1,67.04 130 | 33,3,23.7,1,160.81 131 | 33,2,23.7,0,69.51 132 | 33,1,23.7,1,90.1 133 | 33,0,23.7,1,26.8 134 | 34,3,19.05,1,139.04 135 | 34,2,19.05,0,40.06 136 | 34,1,19.05,1,49.55 137 | 34,0,19.05,0,0.0 138 | 35,3,13.26,1,105.0 139 | 35,2,13.26,0,12.24 140 | 35,1,13.26,1,45.97 141 | 35,0,13.26,0,0.0 142 | 36,3,42.96,1,340.51 143 | 36,2,42.96,1,248.49 144 | 36,1,42.96,1,167.35 145 | 36,0,42.96,1,91.38 146 | 37,3,22.69,1,134.13 147 | 37,2,22.69,0,38.52 148 | 37,1,22.69,1,37.3 149 | 37,0,22.69,1,5.49 150 | 38,3,20.15,0,139.36 151 | 38,2,20.15,1,155.85 152 | 38,1,20.15,0,70.08 153 | 38,0,20.15,1,69.1 154 | 39,3,17.7,1,252.23 155 | 39,2,17.7,0,165.81 156 | 39,1,17.7,0,122.79 157 | 39,0,17.7,0,108.51 158 | 40,3,22.66,1,234.57 159 | 40,2,22.66,0,122.69 160 | 40,1,22.66,1,153.78 161 | 40,0,22.66,1,104.43 162 | 41,3,20.3,1,182.05 163 | 41,2,20.3,1,136.92 164 | 41,1,20.3,1,99.29 165 | 41,0,20.3,0,0.0 166 | 42,3,22.03,1,156.08 167 | 42,2,22.03,1,108.96 168 | 42,1,22.03,0,24.1 169 | 42,0,22.03,1,0.0 170 | 43,3,21.37,0,99.56 171 | 43,2,21.37,1,115.46 172 | 43,1,21.37,0,10.74 173 | 43,0,21.37,0,0.0 174 | 44,3,19.02,1,255.1 175 | 44,2,19.02,1,210.21 176 | 44,1,19.02,1,178.06 177 | 44,0,19.02,0,88.33 178 | 45,3,18.86,1,151.65 179 | 45,2,18.86,1,125.63 180 | 45,1,18.86,1,86.75 181 | 45,0,18.86,0,13.0 182 | 46,3,24.96,1,200.96 183 | 46,2,24.96,1,124.51 184 | 46,1,24.96,0,29.01 185 | 46,0,24.96,1,32.11 186 | 47,3,15.13,1,165.05 187 | 47,2,15.13,1,120.46 188 | 47,1,15.13,1,111.71 189 | 47,0,15.13,0,1.33 190 | 48,3,19.37,1,281.93 191 | 48,2,19.37,1,230.06 192 | 48,1,19.37,1,194.48 193 | 48,0,19.37,1,166.23 194 | 49,3,25.28,1,234.07 195 | 49,2,25.28,1,167.0 196 | 49,1,25.28,1,123.27 197 | 49,0,25.28,1,75.97 198 | 50,3,19.54,0,161.51 199 | 50,2,19.54,1,168.65 200 | 50,1,19.54,0,79.88 201 | 50,0,19.54,0,42.28 202 | 51,3,20.3,1,193.35 203 | 51,2,20.3,0,98.05 204 | 51,1,20.3,0,68.81 205 | 51,0,20.3,0,30.87 206 | 52,3,10.49,0,43.76 207 | 52,2,10.49,0,18.38 208 | 52,1,10.49,0,19.19 209 | 52,0,10.49,0,0.0 210 | 53,3,17.14,1,104.21 211 | 53,2,17.14,0,25.03 212 | 53,1,17.14,0,0.0 213 | 53,0,17.14,0,0.0 214 | 54,3,18.33,0,102.28 215 | 54,2,18.33,1,129.24 216 | 54,1,18.33,0,26.28 217 | 54,0,18.33,0,0.0 218 | 55,3,22.02,1,175.74 219 | 55,2,22.02,0,76.97 220 | 55,1,22.02,0,44.61 221 | 55,0,22.02,1,53.78 222 | 56,3,28.27,1,270.4 223 | 56,2,28.27,1,224.97 224 | 56,1,28.27,0,125.06 225 | 56,0,28.27,1,89.09 226 | 57,3,22.75,1,249.27 227 | 57,2,22.75,1,184.94 228 | 57,1,22.75,1,143.8 229 | 57,0,22.75,1,117.3 230 | 58,3,24.37,1,279.62 231 | 58,2,24.37,1,203.51 232 | 58,1,24.37,1,156.95 233 | 58,0,24.37,1,115.57 234 | 59,3,15.28,0,113.34 235 | 59,2,15.28,1,135.11 236 | 59,1,15.28,0,38.78 237 | 59,0,15.28,0,16.81 238 | 60,3,21.95,0,198.08 239 | 60,2,21.95,1,188.82 240 | 60,1,21.95,1,139.28 241 | 60,0,21.95,1,115.44 242 | 61,3,13.87,1,114.21 243 | 61,2,13.87,0,0.0 244 | 61,1,13.87,0,0.0 245 | 61,0,13.87,0,0.0 246 | 62,3,17.46,1,216.47 247 | 62,2,17.46,0,142.52 248 | 62,1,17.46,1,157.97 249 | 62,0,17.46,0,73.21 250 | 63,3,14.27,0,113.9 251 | 63,2,14.27,1,134.35 252 | 63,1,14.27,1,125.91 253 | 63,0,14.27,0,20.31 254 | 64,3,26.94,1,223.79 255 | 64,2,26.94,1,187.78 256 | 64,1,26.94,1,115.08 257 | 64,0,26.94,1,69.66 258 | 65,3,23.36,1,232.71 259 | 65,2,23.36,1,164.75 260 | 65,1,23.36,0,86.33 261 | 65,0,23.36,0,39.63 262 | 66,3,16.86,1,171.05 263 | 66,2,16.86,0,75.66 264 | 66,1,16.86,0,43.07 265 | 66,0,16.86,0,3.5 266 | 67,3,25.62,0,226.26 267 | 67,2,25.62,1,231.46 268 | 67,1,25.62,1,176.71 269 | 67,0,25.62,1,112.66 270 | 68,3,15.35,1,232.0 271 | 68,2,15.35,1,189.42 272 | 68,1,15.35,0,120.94 273 | 68,0,15.35,0,77.07 274 | 69,3,21.46,1,285.75 275 | 69,2,21.46,1,265.48 276 | 69,1,21.46,1,196.78 277 | 69,0,21.46,0,128.53 278 | 70,3,12.64,0,0.0 279 | 70,2,12.64,0,0.0 280 | 70,1,12.64,0,0.0 281 | 70,0,12.64,0,0.0 282 | 71,3,23.86,0,76.19 283 | 71,2,23.86,1,95.02 284 | 71,1,23.86,1,6.55 285 | 71,0,23.86,1,0.0 286 | 72,3,20.52,0,100.49 287 | 72,2,20.52,1,118.06 288 | 72,1,20.52,0,18.79 289 | 72,0,20.52,0,0.0 290 | 73,3,23.15,0,163.72 291 | 73,2,23.15,0,110.7 292 | 73,1,23.15,1,126.03 293 | 73,0,23.15,0,26.38 294 | 74,3,13.7,0,125.87 295 | 74,2,13.7,0,103.95 296 | 74,1,13.7,1,122.44 297 | 74,0,13.7,0,67.88 298 | 75,3,19.49,0,131.53 299 | 75,2,19.49,0,116.3 300 | 75,1,19.49,1,115.05 301 | 75,0,19.49,0,18.41 302 | 76,3,18.67,1,70.65 303 | 76,2,18.67,1,21.01 304 | 76,1,18.67,0,0.0 305 | 76,0,18.67,0,0.0 306 | 77,3,17.15,0,91.2 307 | 77,2,17.15,1,114.81 308 | 77,1,17.15,0,29.61 309 | 77,0,17.15,0,0.0 310 | 78,3,22.02,1,238.1 311 | 78,2,22.02,0,154.01 312 | 78,1,22.02,1,149.54 313 | 78,0,22.02,0,56.21 314 | 79,3,24.37,1,208.39 315 | 79,2,24.37,1,183.04 316 | 79,1,24.37,1,115.47 317 | 79,0,24.37,1,72.61 318 | 80,3,24.81,1,256.28 319 | 80,2,24.81,1,213.01 320 | 80,1,24.81,0,114.81 321 | 80,0,24.81,0,49.7 322 | 81,3,15.41,0,91.72 323 | 81,2,15.41,0,49.13 324 | 81,1,15.41,0,43.33 325 | 81,0,15.41,0,0.0 326 | 82,3,23.1,1,186.99 327 | 82,2,23.1,0,78.19 328 | 82,1,23.1,0,60.29 329 | 82,0,23.1,1,61.42 330 | 83,3,17.49,1,157.3 331 | 83,2,17.49,1,140.62 332 | 83,1,17.49,1,99.06 333 | 83,0,17.49,1,74.64 334 | 84,3,18.94,1,187.69 335 | 84,2,18.94,1,129.58 336 | 84,1,18.94,1,126.81 337 | 84,0,18.94,1,68.68 338 | 85,3,27.51,1,238.04 339 | 85,2,27.51,1,184.98 340 | 85,1,27.51,1,113.7 341 | 85,0,27.51,1,68.7 342 | 86,3,25.52,0,166.61 343 | 86,2,25.52,1,176.86 344 | 86,1,25.52,1,112.15 345 | 86,0,25.52,1,88.36 346 | 87,3,24.8,1,176.74 347 | 87,2,24.8,0,79.41 348 | 87,1,24.8,1,83.01 349 | 87,0,24.8,1,12.2 350 | 88,3,19.85,0,154.8 351 | 88,2,19.85,0,109.38 352 | 88,1,19.85,1,125.78 353 | 88,0,19.85,0,43.25 354 | 89,3,19.2,0,118.64 355 | 89,2,19.2,1,146.11 356 | 89,1,19.2,0,57.66 357 | 89,0,19.2,0,19.92 358 | 90,3,9.7,0,113.56 359 | 90,2,9.7,1,143.33 360 | 90,1,9.7,0,64.6 361 | 90,0,9.7,0,53.71 362 | 91,3,22.9,1,266.01 363 | 91,2,22.9,1,218.52 364 | 91,1,22.9,1,181.97 365 | 91,0,22.9,0,69.39 366 | 92,3,17.62,1,182.28 367 | 92,2,17.62,1,147.75 368 | 92,1,17.62,1,101.84 369 | 92,0,17.62,0,16.77 370 | 93,3,15.37,1,120.3 371 | 93,2,15.37,0,44.92 372 | 93,1,15.37,0,1.05 373 | 93,0,15.37,0,0.0 374 | 94,3,21.1,0,107.35 375 | 94,2,21.1,0,49.32 376 | 94,1,21.1,1,101.41 377 | 94,0,21.1,1,26.39 378 | 95,3,21.54,1,204.26 379 | 95,2,21.54,1,172.73 380 | 95,1,21.54,0,83.13 381 | 95,0,21.54,0,37.22 382 | 96,3,23.73,1,188.81 383 | 96,2,23.73,1,170.49 384 | 96,1,23.73,1,92.01 385 | 96,0,23.73,0,38.62 386 | 97,3,21.11,0,124.87 387 | 97,2,21.11,1,140.41 388 | 97,1,21.11,1,90.54 389 | 97,0,21.11,0,19.01 390 | 98,3,19.58,1,179.77 391 | 98,2,19.58,0,87.13 392 | 98,1,19.58,1,113.09 393 | 98,0,19.58,1,57.14 394 | 99,3,21.48,0,125.7 395 | 99,2,21.48,1,125.63 396 | 99,1,21.48,0,29.15 397 | 99,0,21.48,0,0.0 398 | 100,3,15.57,1,180.51 399 | 100,2,15.57,1,141.41 400 | 100,1,15.57,0,70.57 401 | 100,0,15.57,1,94.88 402 | 101,3,29.47,1,223.35 403 | 101,2,29.47,1,176.93 404 | 101,1,29.47,1,95.58 405 | 101,0,29.47,1,56.53 406 | 102,3,14.12,0,148.0 407 | 102,2,14.12,1,178.9 408 | 102,1,14.12,1,137.2 409 | 102,0,14.12,0,84.93 410 | 103,3,23.19,0,162.6 411 | 103,2,23.19,1,190.44 412 | 103,1,23.19,0,82.44 413 | 103,0,23.19,1,96.53 414 | 104,3,20.3,0,155.53 415 | 104,2,20.3,1,142.53 416 | 104,1,20.3,1,137.86 417 | 104,0,20.3,0,36.45 418 | 105,3,15.84,1,95.38 419 | 105,2,15.84,1,86.67 420 | 105,1,15.84,0,0.0 421 | 105,0,15.84,0,0.0 422 | 106,3,18.87,0,131.24 423 | 106,2,18.87,1,162.43 424 | 106,1,18.87,1,87.26 425 | 106,0,18.87,0,21.51 426 | 107,3,18.5,1,205.7 427 | 107,2,18.5,0,120.24 428 | 107,1,18.5,0,68.65 429 | 107,0,18.5,1,87.33 430 | 108,3,19.09,1,119.99 431 | 108,2,19.09,0,44.3 432 | 108,1,19.09,0,8.63 433 | 108,0,19.09,0,0.0 434 | 109,3,14.88,1,138.21 435 | 109,2,14.88,1,91.58 436 | 109,1,14.88,0,33.1 437 | 109,0,14.88,0,0.0 438 | 110,3,17.7,1,177.52 439 | 110,2,17.7,1,142.68 440 | 110,1,17.7,0,50.27 441 | 110,0,17.7,0,6.02 442 | 111,3,19.4,0,47.13 443 | 111,2,19.4,1,72.84 444 | 111,1,19.4,0,0.0 445 | 111,0,19.4,0,0.0 446 | 112,3,26.82,1,244.61 447 | 112,2,26.82,1,165.02 448 | 112,1,26.82,1,154.35 449 | 112,0,26.82,1,78.73 450 | 113,3,14.27,0,122.46 451 | 113,2,14.27,0,104.87 452 | 113,1,14.27,0,82.73 453 | 113,0,14.27,0,43.91 454 | 114,3,26.03,1,166.67 455 | 114,2,26.03,1,145.03 456 | 114,1,26.03,1,63.58 457 | 114,0,26.03,0,0.0 458 | 115,3,17.15,1,186.79 459 | 115,2,17.15,0,90.46 460 | 115,1,17.15,0,44.27 461 | 115,0,17.15,0,18.1 462 | 116,3,11.11,0,0.0 463 | 116,2,11.11,1,39.24 464 | 116,1,11.11,0,0.0 465 | 116,0,11.11,0,0.0 466 | 117,3,20.97,0,136.19 467 | 117,2,20.97,0,105.2 468 | 117,1,20.97,1,97.82 469 | 117,0,20.97,0,0.0 470 | 118,3,27.09,1,140.84 471 | 118,2,27.09,1,88.98 472 | 118,1,27.09,1,37.71 473 | 118,0,27.09,1,0.0 474 | 119,3,21.08,0,213.67 475 | 119,2,21.08,1,203.36 476 | 119,1,21.08,1,168.71 477 | 119,0,21.08,0,50.14 478 | 120,3,18.82,0,156.61 479 | 120,2,18.82,0,86.1 480 | 120,1,18.82,1,120.81 481 | 120,0,18.82,1,79.62 482 | 121,3,24.17,1,190.54 483 | 121,2,24.17,0,101.04 484 | 121,1,24.17,1,116.87 485 | 121,0,24.17,1,58.64 486 | 122,3,17.52,0,73.55 487 | 122,2,17.52,1,95.33 488 | 122,1,17.52,0,0.0 489 | 122,0,17.52,1,20.64 490 | 123,3,19.75,1,240.46 491 | 123,2,19.75,1,195.57 492 | 123,1,19.75,0,112.58 493 | 123,0,19.75,1,140.22 494 | 124,3,20.15,1,272.73 495 | 124,2,20.15,1,240.82 496 | 124,1,20.15,0,148.09 497 | 124,0,20.15,0,101.77 498 | 125,3,19.8,1,86.64 499 | 125,2,19.8,0,0.0 500 | 125,1,19.8,0,0.0 501 | 125,0,19.8,1,0.0 502 | 126,3,24.27,0,224.34 503 | 126,2,24.27,1,217.11 504 | 126,1,24.27,1,173.4 505 | 126,0,24.27,0,73.44 506 | 127,3,23.06,0,221.76 507 | 127,2,23.06,1,229.86 508 | 127,1,23.06,1,180.86 509 | 127,0,23.06,0,87.99 510 | 128,3,18.03,0,118.64 511 | 128,2,18.03,1,141.78 512 | 128,1,18.03,0,73.2 513 | 128,0,18.03,0,18.86 514 | 129,3,15.5,1,84.51 515 | 129,2,15.5,0,9.72 516 | 129,1,15.5,0,0.0 517 | 129,0,15.5,0,0.0 518 | 130,3,9.96,1,158.38 519 | 130,2,9.96,1,125.66 520 | 130,1,9.96,1,117.69 521 | 130,0,9.96,0,35.7 522 | 131,3,14.76,0,86.86 523 | 131,2,14.76,0,47.85 524 | 131,1,14.76,0,14.8 525 | 131,0,14.76,0,9.92 526 | 132,3,13.54,0,44.29 527 | 132,2,13.54,0,19.28 528 | 132,1,13.54,0,0.0 529 | 132,0,13.54,0,0.0 530 | 133,3,17.07,1,79.08 531 | 133,2,17.07,0,12.35 532 | 133,1,17.07,0,0.0 533 | 133,0,17.07,0,0.0 534 | 134,3,22.12,1,226.08 535 | 134,2,22.12,1,178.54 536 | 134,1,22.12,0,92.5 537 | 134,0,22.12,0,39.04 538 | 135,3,19.68,0,170.22 539 | 135,2,19.68,1,174.21 540 | 135,1,19.68,0,92.65 541 | 135,0,19.68,0,52.66 542 | 136,3,27.3,0,163.69 543 | 136,2,27.3,0,108.58 544 | 136,1,27.3,1,92.41 545 | 136,0,27.3,1,25.3 546 | 137,3,19.67,1,187.64 547 | 137,2,19.67,0,109.99 548 | 137,1,19.67,1,120.89 549 | 137,0,19.67,0,50.54 550 | 138,3,25.98,1,144.3 551 | 138,2,25.98,1,92.15 552 | 138,1,25.98,1,58.99 553 | 138,0,25.98,1,22.63 554 | 139,3,17.78,1,199.9 555 | 139,2,17.78,0,126.73 556 | 139,1,17.78,1,123.71 557 | 139,0,17.78,1,81.98 558 | 140,3,15.74,0,102.79 559 | 140,2,15.74,1,108.9 560 | 140,1,15.74,0,75.15 561 | 140,0,15.74,0,7.52 562 | 141,3,16.71,1,129.94 563 | 141,2,16.71,0,74.84 564 | 141,1,16.71,0,42.34 565 | 141,0,16.71,0,0.0 566 | 142,3,27.31,1,227.66 567 | 142,2,27.31,1,174.45 568 | 142,1,27.31,0,67.63 569 | 142,0,27.31,1,53.2 570 | 143,3,15.0,0,138.41 571 | 143,2,15.0,0,116.54 572 | 143,1,15.0,1,130.25 573 | 143,0,15.0,0,66.27 574 | 144,3,15.22,0,125.08 575 | 144,2,15.22,1,135.63 576 | 144,1,15.22,0,81.78 577 | 144,0,15.22,0,31.89 578 | 145,3,28.21,0,220.89 579 | 145,2,28.21,1,198.11 580 | 145,1,28.21,0,97.31 581 | 145,0,28.21,1,77.49 582 | 146,3,21.96,1,219.5 583 | 146,2,21.96,1,134.4 584 | 146,1,21.96,0,62.37 585 | 146,0,21.96,0,3.95 586 | 147,3,20.74,0,140.88 587 | 147,2,20.74,0,102.07 588 | 147,1,20.74,0,48.34 589 | 147,0,20.74,1,68.96 590 | 148,3,11.61,1,165.74 591 | 148,2,11.61,0,71.67 592 | 148,1,11.61,1,111.4 593 | 148,0,11.61,0,38.97 594 | 149,3,18.07,0,145.04 595 | 149,2,18.07,1,140.04 596 | 149,1,18.07,1,110.86 597 | 149,0,18.07,0,30.21 598 | 150,3,24.21,1,254.77 599 | 150,2,24.21,0,137.88 600 | 150,1,24.21,0,102.58 601 | 150,0,24.21,1,103.89 602 | 151,3,20.09,1,165.18 603 | 151,2,20.09,0,88.17 604 | 151,1,20.09,0,40.32 605 | 151,0,20.09,1,38.44 606 | 152,3,16.1,0,0.0 607 | 152,2,16.1,0,0.0 608 | 152,1,16.1,0,0.0 609 | 152,0,16.1,1,0.0 610 | 153,3,15.81,1,216.01 611 | 153,2,15.81,1,171.83 612 | 153,1,15.81,1,139.04 613 | 153,0,15.81,0,63.66 614 | 154,3,22.83,0,255.64 615 | 154,2,22.83,1,265.16 616 | 154,1,22.83,1,232.45 617 | 154,0,22.83,0,120.37 618 | 155,3,15.16,0,127.25 619 | 155,2,15.16,0,87.92 620 | 155,1,15.16,1,100.36 621 | 155,0,15.16,0,38.75 622 | 156,3,18.12,1,170.79 623 | 156,2,18.12,0,81.04 624 | 156,1,18.12,0,30.77 625 | 156,0,18.12,0,3.77 626 | 157,3,21.99,1,191.78 627 | 157,2,21.99,1,117.73 628 | 157,1,21.99,0,58.39 629 | 157,0,21.99,1,46.48 630 | 158,3,16.3,0,97.53 631 | 158,2,16.3,0,56.66 632 | 158,1,16.3,0,32.4 633 | 158,0,16.3,0,0.0 634 | 159,3,19.89,1,160.83 635 | 159,2,19.89,0,97.57 636 | 159,1,19.89,0,64.68 637 | 159,0,19.89,0,13.2 638 | 160,3,17.14,1,104.37 639 | 160,2,17.14,1,65.52 640 | 160,1,17.14,0,0.0 641 | 160,0,17.14,0,0.0 642 | 161,3,23.74,1,181.28 643 | 161,2,23.74,1,143.72 644 | 161,1,23.74,1,81.79 645 | 161,0,23.74,0,0.0 646 | 162,3,21.62,0,91.64 647 | 162,2,21.62,1,97.81 648 | 162,1,21.62,1,69.66 649 | 162,0,21.62,0,0.0 650 | 163,3,19.89,1,206.33 651 | 163,2,19.89,0,106.11 652 | 163,1,19.89,1,97.21 653 | 163,0,19.89,1,77.47 654 | 164,3,25.0,0,142.43 655 | 164,2,25.0,1,151.04 656 | 164,1,25.0,0,28.58 657 | 164,0,25.0,0,0.72 658 | 165,3,16.71,1,194.59 659 | 165,2,16.71,1,142.43 660 | 165,1,16.71,1,115.1 661 | 165,0,16.71,0,38.93 662 | 166,3,23.5,1,190.71 663 | 166,2,23.5,1,127.83 664 | 166,1,23.5,0,51.61 665 | 166,0,23.5,1,47.65 666 | 167,3,23.33,1,249.49 667 | 167,2,23.33,1,210.54 668 | 167,1,23.33,1,167.44 669 | 167,0,23.33,0,59.03 670 | 168,3,19.26,0,147.44 671 | 168,2,19.26,0,95.29 672 | 168,1,19.26,0,59.62 673 | 168,0,19.26,0,3.96 674 | 169,3,18.63,0,232.07 675 | 169,2,18.63,0,164.69 676 | 169,1,18.63,1,205.66 677 | 169,0,18.63,1,156.92 678 | 170,3,16.5,0,30.73 679 | 170,2,16.5,1,27.0 680 | 170,1,16.5,1,2.4 681 | 170,0,16.5,0,0.0 682 | 171,3,29.07,1,222.93 683 | 171,2,29.07,1,148.6 684 | 171,1,29.07,1,76.81 685 | 171,0,29.07,1,34.83 686 | 172,3,17.25,1,213.76 687 | 172,2,17.25,0,104.13 688 | 172,1,17.25,0,84.0 689 | 172,0,17.25,0,49.67 690 | 173,3,15.32,0,214.29 691 | 173,2,15.32,1,253.91 692 | 173,1,15.32,0,146.28 693 | 173,0,15.32,0,133.91 694 | 174,3,32.28,0,228.9 695 | 174,2,32.28,1,200.44 696 | 174,1,32.28,0,69.71 697 | 174,0,32.28,1,50.69 698 | 175,3,20.78,1,198.79 699 | 175,2,20.78,0,76.74 700 | 175,1,20.78,1,113.31 701 | 175,0,20.78,0,10.5 702 | 176,3,18.26,1,134.84 703 | 176,2,18.26,0,72.86 704 | 176,1,18.26,0,27.94 705 | 176,0,18.26,1,49.32 706 | 177,3,19.53,0,206.54 707 | 177,2,19.53,1,193.04 708 | 177,1,19.53,0,125.54 709 | 177,0,19.53,1,127.14 710 | 178,3,19.13,0,99.65 711 | 178,2,19.13,0,54.51 712 | 178,1,19.13,0,33.01 713 | 178,0,19.13,0,2.62 714 | 179,3,16.8,1,203.17 715 | 179,2,16.8,0,101.24 716 | 179,1,16.8,0,74.11 717 | 179,0,16.8,0,44.03 718 | 180,3,15.3,1,120.06 719 | 180,2,15.3,0,51.33 720 | 180,1,15.3,0,28.5 721 | 180,0,15.3,0,0.0 722 | 181,3,19.33,0,74.71 723 | 181,2,19.33,1,83.97 724 | 181,1,19.33,0,0.0 725 | 181,0,19.33,1,0.0 726 | 182,3,19.59,1,179.68 727 | 182,2,19.59,0,96.67 728 | 182,1,19.59,0,71.17 729 | 182,0,19.59,1,68.16 730 | 183,3,17.12,1,186.97 731 | 183,2,17.12,1,151.24 732 | 183,1,17.12,0,77.03 733 | 183,0,17.12,0,25.12 734 | 184,3,20.32,1,252.69 735 | 184,2,20.32,1,202.05 736 | 184,1,20.32,0,105.11 737 | 184,0,20.32,1,109.08 738 | 185,3,22.31,1,195.03 739 | 185,2,22.31,0,77.2 740 | 185,1,22.31,0,39.27 741 | 185,0,22.31,0,0.0 742 | 186,3,19.01,0,99.44 743 | 186,2,19.01,0,41.55 744 | 186,1,19.01,0,18.8 745 | 186,0,19.01,0,0.0 746 | 187,3,24.01,1,237.13 747 | 187,2,24.01,1,183.83 748 | 187,1,24.01,0,93.42 749 | 187,0,24.01,1,96.23 750 | 188,3,17.79,1,162.49 751 | 188,2,17.79,0,102.33 752 | 188,1,17.79,0,62.87 753 | 188,0,17.79,1,84.5 754 | 189,3,20.5,1,208.65 755 | 189,2,20.5,1,151.09 756 | 189,1,20.5,1,133.59 757 | 189,0,20.5,1,76.47 758 | 190,3,27.07,1,217.03 759 | 190,2,27.07,1,191.3 760 | 190,1,27.07,0,61.86 761 | 190,0,27.07,1,38.79 762 | 191,3,22.82,1,277.32 763 | 191,2,22.82,1,208.23 764 | 191,1,22.82,0,106.29 765 | 191,0,22.82,0,68.77 766 | 192,3,22.48,1,219.71 767 | 192,2,22.48,1,178.38 768 | 192,1,22.48,0,89.51 769 | 192,0,22.48,1,87.13 770 | 193,3,31.69,1,358.41 771 | 193,2,31.69,1,286.82 772 | 193,1,31.69,1,221.62 773 | 193,0,31.69,1,170.02 774 | 194,3,18.73,1,222.5 775 | 194,2,18.73,1,187.1 776 | 194,1,18.73,1,156.84 777 | 194,0,18.73,0,56.71 778 | 195,3,24.55,1,253.83 779 | 195,2,24.55,1,189.26 780 | 195,1,24.55,0,90.59 781 | 195,0,24.55,1,89.27 782 | 196,3,22.09,0,59.43 783 | 196,2,22.09,0,5.39 784 | 196,1,22.09,0,0.0 785 | 196,0,22.09,0,0.0 786 | 197,3,17.63,1,189.82 787 | 197,2,17.63,1,171.28 788 | 197,1,17.63,1,135.12 789 | 197,0,17.63,0,41.68 790 | 198,3,22.8,1,217.44 791 | 198,2,22.8,1,169.21 792 | 198,1,22.8,0,68.51 793 | 198,0,22.8,0,12.76 794 | 199,3,32.52,1,301.33 795 | 199,2,32.52,1,239.9 796 | 199,1,32.52,1,187.12 797 | 199,0,32.52,0,49.83 798 | 200,3,22.52,1,262.92 799 | 200,2,22.52,0,151.67 800 | 200,1,22.52,1,159.24 801 | 200,0,22.52,0,65.89 802 | 201,3,18.16,0,107.03 803 | 201,2,18.16,0,65.86 804 | 201,1,18.16,0,12.62 805 | 201,0,18.16,0,0.0 806 | 202,3,21.92,1,231.78 807 | 202,2,21.92,0,147.24 808 | 202,1,21.92,1,136.75 809 | 202,0,21.92,1,109.04 810 | 203,3,14.11,0,52.09 811 | 203,2,14.11,0,21.88 812 | 203,1,14.11,0,0.0 813 | 203,0,14.11,0,0.0 814 | 204,3,20.87,1,286.28 815 | 204,2,20.87,1,238.55 816 | 204,1,20.87,0,159.49 817 | 204,0,20.87,1,177.1 818 | 205,3,18.53,0,54.25 819 | 205,2,18.53,0,21.25 820 | 205,1,18.53,0,0.0 821 | 205,0,18.53,1,0.0 822 | 206,3,24.75,0,74.8 823 | 206,2,24.75,0,44.23 824 | 206,1,24.75,0,0.0 825 | 206,0,24.75,0,0.0 826 | 207,3,13.01,1,58.71 827 | 207,2,13.01,1,22.22 828 | 207,1,13.01,0,0.0 829 | 207,0,13.01,1,0.0 830 | 208,3,19.89,0,70.47 831 | 208,2,19.89,0,33.78 832 | 208,1,19.89,1,61.4 833 | 208,0,19.89,0,0.0 834 | 209,3,22.69,1,164.86 835 | 209,2,22.69,1,86.5 836 | 209,1,22.69,0,11.34 837 | 209,0,22.69,1,14.07 838 | 210,3,20.41,0,237.4 839 | 210,2,20.41,0,197.1 840 | 210,1,20.41,0,145.89 841 | 210,0,20.41,1,161.35 842 | 211,3,23.32,1,196.03 843 | 211,2,23.32,1,156.51 844 | 211,1,23.32,0,28.11 845 | 211,0,23.32,1,53.01 846 | 212,3,20.09,0,68.69 847 | 212,2,20.09,0,36.02 848 | 212,1,20.09,1,34.55 849 | 212,0,20.09,0,0.0 850 | 213,3,20.03,1,233.85 851 | 213,2,20.03,1,198.76 852 | 213,1,20.03,0,115.8 853 | 213,0,20.03,0,55.17 854 | 214,3,17.7,1,139.43 855 | 214,2,17.7,0,53.08 856 | 214,1,17.7,0,23.77 857 | 214,0,17.7,0,0.0 858 | 215,3,28.24,1,182.68 859 | 215,2,28.24,1,113.02 860 | 215,1,28.24,1,49.56 861 | 215,0,28.24,1,15.7 862 | 216,3,21.49,1,225.26 863 | 216,2,21.49,1,208.81 864 | 216,1,21.49,1,151.05 865 | 216,0,21.49,0,51.3 866 | 217,3,18.83,1,207.05 867 | 217,2,18.83,0,115.11 868 | 217,1,18.83,1,143.38 869 | 217,0,18.83,0,44.07 870 | 218,3,13.55,1,106.9 871 | 218,2,13.55,0,44.14 872 | 218,1,13.55,0,10.71 873 | 218,0,13.55,0,0.0 874 | 219,3,22.35,1,267.92 875 | 219,2,22.35,1,237.77 876 | 219,1,22.35,1,178.11 877 | 219,0,22.35,1,131.73 878 | 220,3,21.94,1,199.19 879 | 220,2,21.94,1,162.03 880 | 220,1,21.94,0,76.08 881 | 220,0,21.94,0,13.77 882 | 221,3,13.76,0,176.94 883 | 221,2,13.76,0,144.67 884 | 221,1,13.76,1,147.35 885 | 221,0,13.76,0,87.47 886 | 222,3,22.51,1,226.32 887 | 222,2,22.51,1,181.63 888 | 222,1,22.51,1,134.22 889 | 222,0,22.51,1,77.23 890 | 223,3,23.16,1,268.96 891 | 223,2,23.16,0,179.88 892 | 223,1,23.16,0,145.61 893 | 223,0,23.16,1,135.86 894 | 224,3,29.49,0,220.32 895 | 224,2,29.49,1,194.61 896 | 224,1,29.49,1,135.21 897 | 224,0,29.49,1,69.68 898 | 225,3,17.1,1,89.89 899 | 225,2,17.1,1,65.1 900 | 225,1,17.1,0,0.0 901 | 225,0,17.1,0,0.0 902 | 226,3,25.28,1,337.43 903 | 226,2,25.28,1,293.57 904 | 226,1,25.28,1,253.08 905 | 226,0,25.28,1,191.15 906 | 227,3,24.8,1,191.66 907 | 227,2,24.8,1,136.66 908 | 227,1,24.8,0,42.41 909 | 227,0,24.8,1,31.96 910 | 228,3,23.21,0,153.54 911 | 228,2,23.21,0,82.57 912 | 228,1,23.21,0,56.67 913 | 228,0,23.21,0,0.0 914 | 229,3,22.94,1,179.07 915 | 229,2,22.94,1,116.2 916 | 229,1,22.94,1,73.18 917 | 229,0,22.94,0,0.0 918 | 230,3,17.97,1,151.76 919 | 230,2,17.97,0,53.49 920 | 230,1,17.97,1,56.7 921 | 230,0,17.97,1,43.07 922 | 231,3,14.91,1,146.28 923 | 231,2,14.91,0,81.18 924 | 231,1,14.91,0,45.19 925 | 231,0,14.91,0,8.19 926 | 232,3,26.44,1,235.91 927 | 232,2,26.44,1,201.37 928 | 232,1,26.44,1,139.41 929 | 232,0,26.44,1,76.25 930 | 233,3,22.46,1,247.65 931 | 233,2,22.46,1,195.13 932 | 233,1,22.46,0,94.76 933 | 233,0,22.46,0,54.76 934 | 234,3,18.42,1,244.59 935 | 234,2,18.42,1,237.39 936 | 234,1,18.42,0,132.98 937 | 234,0,18.42,1,153.41 938 | 235,3,16.05,0,86.63 939 | 235,2,16.05,1,98.57 940 | 235,1,16.05,0,18.31 941 | 235,0,16.05,0,0.0 942 | 236,3,27.83,1,171.77 943 | 236,2,27.83,1,122.69 944 | 236,1,27.83,1,71.13 945 | 236,0,27.83,1,1.99 946 | 237,3,23.74,0,98.82 947 | 237,2,23.74,1,108.84 948 | 237,1,23.74,1,57.59 949 | 237,0,23.74,1,0.3 950 | 238,3,18.68,0,77.34 951 | 238,2,18.68,0,47.38 952 | 238,1,18.68,0,0.0 953 | 238,0,18.68,0,0.0 954 | 239,3,13.51,1,51.9 955 | 239,2,13.51,0,0.0 956 | 239,1,13.51,0,0.0 957 | 239,0,13.51,0,0.0 958 | 240,3,24.09,0,220.25 959 | 240,2,24.09,0,131.62 960 | 240,1,24.09,1,148.96 961 | 240,0,24.09,1,86.98 962 | 241,3,24.19,0,143.04 963 | 241,2,24.19,1,129.98 964 | 241,1,24.19,0,29.8 965 | 241,0,24.19,0,4.71 966 | 242,3,24.8,1,204.38 967 | 242,2,24.8,0,98.08 968 | 242,1,24.8,1,104.21 969 | 242,0,24.8,1,64.71 970 | 243,3,18.75,1,169.15 971 | 243,2,18.75,0,76.76 972 | 243,1,18.75,0,58.54 973 | 243,0,18.75,0,16.93 974 | 244,3,20.29,0,71.47 975 | 244,2,20.29,1,102.69 976 | 244,1,20.29,0,0.0 977 | 244,0,20.29,0,0.0 978 | 245,3,14.02,1,101.02 979 | 245,2,14.02,0,25.25 980 | 245,1,14.02,0,0.0 981 | 245,0,14.02,0,0.0 982 | 246,3,25.59,1,243.71 983 | 246,2,25.59,1,191.16 984 | 246,1,25.59,1,173.67 985 | 246,0,25.59,0,56.0 986 | 247,3,15.05,0,119.66 987 | 247,2,15.05,0,102.69 988 | 247,1,15.05,0,77.39 989 | 247,0,15.05,0,30.3 990 | 248,3,18.83,1,127.26 991 | 248,2,18.83,0,73.3 992 | 248,1,18.83,1,75.0 993 | 248,0,18.83,0,0.0 994 | 249,3,20.72,1,275.24 995 | 249,2,20.72,0,185.23 996 | 249,1,20.72,0,156.37 997 | 249,0,20.72,0,109.26 998 | 250,3,15.75,1,102.34 999 | 250,2,15.75,0,0.9 1000 | 250,1,15.75,1,37.18 1001 | 250,0,15.75,1,0.0 1002 | 251,3,17.85,0,87.57 1003 | 251,2,17.85,1,119.5 1004 | 251,1,17.85,1,83.38 1005 | 251,0,17.85,0,0.0 1006 | 252,3,21.72,0,118.68 1007 | 252,2,21.72,0,64.51 1008 | 252,1,21.72,1,94.11 1009 | 252,0,21.72,1,39.77 1010 | 253,3,25.09,0,39.88 1011 | 253,2,25.09,0,0.0 1012 | 253,1,25.09,0,0.0 1013 | 253,0,25.09,1,0.0 1014 | 254,3,18.3,1,251.0 1015 | 254,2,18.3,0,197.01 1016 | 254,1,18.3,0,133.64 1017 | 254,0,18.3,1,160.08 1018 | 255,3,14.28,0,113.68 1019 | 255,2,14.28,0,70.4 1020 | 255,1,14.28,0,65.13 1021 | 255,0,14.28,1,62.83 1022 | 256,3,14.46,1,91.96 1023 | 256,2,14.46,1,86.49 1024 | 256,1,14.46,0,0.0 1025 | 256,0,14.46,1,10.37 1026 | 257,3,20.0,1,288.27 1027 | 257,2,20.0,0,184.02 1028 | 257,1,20.0,0,172.15 1029 | 257,0,20.0,1,164.72 1030 | 258,3,21.11,1,233.69 1031 | 258,2,21.11,0,114.53 1032 | 258,1,21.11,1,140.24 1033 | 258,0,21.11,1,101.2 1034 | 259,3,15.98,1,186.86 1035 | 259,2,15.98,1,123.27 1036 | 259,1,15.98,0,51.13 1037 | 259,0,15.98,0,39.28 1038 | 260,3,16.17,1,137.13 1039 | 260,2,16.17,1,91.76 1040 | 260,1,16.17,0,9.24 1041 | 260,0,16.17,0,0.0 1042 | 261,3,11.13,0,64.5 1043 | 261,2,11.13,1,126.97 1044 | 261,1,11.13,1,69.08 1045 | 261,0,11.13,0,12.67 1046 | 262,3,10.06,1,39.25 1047 | 262,2,10.06,1,3.11 1048 | 262,1,10.06,0,0.0 1049 | 262,0,10.06,1,0.0 1050 | 263,3,17.84,0,153.25 1051 | 263,2,17.84,0,129.93 1052 | 263,1,17.84,0,83.43 1053 | 263,0,17.84,1,103.49 1054 | 264,3,22.35,0,126.95 1055 | 264,2,22.35,1,138.24 1056 | 264,1,22.35,0,27.28 1057 | 264,0,22.35,1,58.79 1058 | 265,3,15.66,0,105.85 1059 | 265,2,15.66,0,81.49 1060 | 265,1,15.66,1,73.67 1061 | 265,0,15.66,0,0.0 1062 | 266,3,18.87,0,184.08 1063 | 266,2,18.87,0,151.1 1064 | 266,1,18.87,1,161.99 1065 | 266,0,18.87,0,72.01 1066 | 267,3,18.7,1,200.08 1067 | 267,2,18.7,1,149.81 1068 | 267,1,18.7,0,54.42 1069 | 267,0,18.7,0,18.97 1070 | 268,3,17.41,1,150.95 1071 | 268,2,17.41,1,134.72 1072 | 268,1,17.41,0,39.95 1073 | 268,0,17.41,0,25.32 1074 | 269,3,17.92,1,189.22 1075 | 269,2,17.92,0,83.35 1076 | 269,1,17.92,1,108.11 1077 | 269,0,17.92,0,22.46 1078 | 270,3,21.43,1,159.7 1079 | 270,2,21.43,0,54.65 1080 | 270,1,21.43,0,14.93 1081 | 270,0,21.43,0,0.0 1082 | 271,3,23.03,1,256.22 1083 | 271,2,23.03,1,186.46 1084 | 271,1,23.03,0,82.39 1085 | 271,0,23.03,1,103.31 1086 | 272,3,11.2,1,205.51 1087 | 272,2,11.2,1,178.78 1088 | 272,1,11.2,0,90.2 1089 | 272,0,11.2,1,135.75 1090 | 273,3,19.36,1,218.44 1091 | 273,2,19.36,0,129.96 1092 | 273,1,19.36,0,81.55 1093 | 273,0,19.36,0,59.34 1094 | 274,3,18.99,1,231.2 1095 | 274,2,18.99,1,162.02 1096 | 274,1,18.99,1,140.77 1097 | 274,0,18.99,0,45.26 1098 | 275,3,10.73,0,9.06 1099 | 275,2,10.73,1,33.96 1100 | 275,1,10.73,0,0.0 1101 | 275,0,10.73,1,1.11 1102 | 276,3,13.7,1,91.47 1103 | 276,2,13.7,0,25.27 1104 | 276,1,13.7,0,0.0 1105 | 276,0,13.7,0,0.0 1106 | 277,3,16.05,0,52.74 1107 | 277,2,16.05,1,38.56 1108 | 277,1,16.05,1,13.19 1109 | 277,0,16.05,0,0.0 1110 | 278,3,22.26,1,265.84 1111 | 278,2,22.26,1,211.64 1112 | 278,1,22.26,1,191.68 1113 | 278,0,22.26,0,72.85 1114 | 279,3,27.18,0,182.51 1115 | 279,2,27.18,1,162.12 1116 | 279,1,27.18,1,96.53 1117 | 279,0,27.18,0,0.65 1118 | 280,3,14.06,0,173.22 1119 | 280,2,14.06,0,135.62 1120 | 280,1,14.06,0,99.87 1121 | 280,0,14.06,0,63.37 1122 | 281,3,25.12,1,190.73 1123 | 281,2,25.12,0,71.3 1124 | 281,1,25.12,1,80.14 1125 | 281,0,25.12,0,0.0 1126 | 282,3,27.46,1,207.82 1127 | 282,2,27.46,1,147.89 1128 | 282,1,27.46,0,66.69 1129 | 282,0,27.46,1,42.82 1130 | 283,3,18.63,0,120.02 1131 | 283,2,18.63,1,141.69 1132 | 283,1,18.63,0,48.74 1133 | 283,0,18.63,0,8.29 1134 | 284,3,12.24,1,176.63 1135 | 284,2,12.24,0,86.83 1136 | 284,1,12.24,0,55.37 1137 | 284,0,12.24,0,62.19 1138 | 285,3,14.91,0,87.76 1139 | 285,2,14.91,0,61.69 1140 | 285,1,14.91,0,34.85 1141 | 285,0,14.91,0,0.0 1142 | 286,3,18.66,0,125.68 1143 | 286,2,18.66,1,150.14 1144 | 286,1,18.66,1,115.08 1145 | 286,0,18.66,0,14.99 1146 | 287,3,17.57,1,154.34 1147 | 287,2,17.57,1,120.22 1148 | 287,1,17.57,0,59.72 1149 | 287,0,17.57,0,0.0 1150 | 288,3,19.83,0,143.18 1151 | 288,2,19.83,0,98.02 1152 | 288,1,19.83,0,55.93 1153 | 288,0,19.83,0,12.55 1154 | 289,3,30.11,1,197.75 1155 | 289,2,30.11,1,131.4 1156 | 289,1,30.11,1,66.95 1157 | 289,0,30.11,1,3.55 1158 | 290,3,21.44,1,195.96 1159 | 290,2,21.44,1,140.5 1160 | 290,1,21.44,0,57.01 1161 | 290,0,21.44,1,52.94 1162 | 291,3,18.13,1,174.41 1163 | 291,2,18.13,0,57.35 1164 | 291,1,18.13,1,88.5 1165 | 291,0,18.13,0,0.0 1166 | 292,3,25.36,1,204.19 1167 | 292,2,25.36,0,128.09 1168 | 292,1,25.36,0,49.22 1169 | 292,0,25.36,1,61.03 1170 | 293,3,16.96,0,137.24 1171 | 293,2,16.96,0,104.6 1172 | 293,1,16.96,0,56.97 1173 | 293,0,16.96,0,25.31 1174 | 294,3,33.38,1,250.72 1175 | 294,2,33.38,1,171.92 1176 | 294,1,33.38,1,100.12 1177 | 294,0,33.38,1,38.74 1178 | 295,3,17.68,1,155.92 1179 | 295,2,17.68,1,116.89 1180 | 295,1,17.68,0,22.33 1181 | 295,0,17.68,0,7.43 1182 | 296,3,11.49,0,44.21 1183 | 296,2,11.49,0,31.41 1184 | 296,1,11.49,0,9.84 1185 | 296,0,11.49,0,0.0 1186 | 297,3,22.46,1,202.19 1187 | 297,2,22.46,0,135.15 1188 | 297,1,22.46,0,77.34 1189 | 297,0,22.46,0,26.64 1190 | 298,3,13.58,1,167.15 1191 | 298,2,13.58,1,149.83 1192 | 298,1,13.58,1,105.69 1193 | 298,0,13.58,0,47.85 1194 | 299,3,13.57,1,88.34 1195 | 299,2,13.57,1,52.83 1196 | 299,1,13.57,0,0.0 1197 | 299,0,13.57,0,0.0 1198 | 300,3,27.62,1,273.2 1199 | 300,2,27.62,1,201.25 1200 | 300,1,27.62,1,165.64 1201 | 300,0,27.62,1,89.64 1202 | 301,3,15.96,1,263.85 1203 | 301,2,15.96,1,233.97 1204 | 301,1,15.96,1,208.09 1205 | 301,0,15.96,1,152.31 1206 | 302,3,12.14,1,45.72 1207 | 302,2,12.14,1,47.43 1208 | 302,1,12.14,0,0.0 1209 | 302,0,12.14,1,0.0 1210 | 303,3,17.17,1,128.87 1211 | 303,2,17.17,0,43.49 1212 | 303,1,17.17,1,52.68 1213 | 303,0,17.17,0,0.0 1214 | 304,3,23.69,0,112.35 1215 | 304,2,23.69,0,85.33 1216 | 304,1,23.69,0,13.85 1217 | 304,0,23.69,1,22.53 1218 | 305,3,27.84,1,216.17 1219 | 305,2,27.84,0,96.08 1220 | 305,1,27.84,1,93.5 1221 | 305,0,27.84,1,45.14 1222 | 306,3,21.55,0,223.93 1223 | 306,2,21.55,0,173.67 1224 | 306,1,21.55,1,180.13 1225 | 306,0,21.55,1,138.39 1226 | 307,3,20.53,0,126.02 1227 | 307,2,20.53,0,80.74 1228 | 307,1,20.53,0,51.06 1229 | 307,0,20.53,0,0.52 1230 | 308,3,18.56,0,75.84 1231 | 308,2,18.56,0,38.7 1232 | 308,1,18.56,1,51.04 1233 | 308,0,18.56,0,0.0 1234 | 309,3,19.05,0,159.36 1235 | 309,2,19.05,1,173.64 1236 | 309,1,19.05,1,132.88 1237 | 309,0,19.05,1,88.4 1238 | 310,3,16.87,0,91.88 1239 | 310,2,16.87,0,36.9 1240 | 310,1,16.87,1,59.83 1241 | 310,0,16.87,0,0.0 1242 | 311,3,20.84,0,192.83 1243 | 311,2,20.84,0,168.86 1244 | 311,1,20.84,1,180.25 1245 | 311,0,20.84,1,130.59 1246 | 312,3,18.62,1,195.42 1247 | 312,2,18.62,1,161.91 1248 | 312,1,18.62,1,101.5 1249 | 312,0,18.62,1,70.85 1250 | 313,3,16.11,1,226.19 1251 | 313,2,16.11,0,130.1 1252 | 313,1,16.11,0,105.11 1253 | 313,0,16.11,1,131.74 1254 | 314,3,27.42,1,201.04 1255 | 314,2,27.42,1,144.78 1256 | 314,1,27.42,1,101.04 1257 | 314,0,27.42,1,40.82 1258 | 315,3,32.16,1,423.01 1259 | 315,2,32.16,1,350.73 1260 | 315,1,32.16,1,271.38 1261 | 315,0,32.16,1,210.67 1262 | 316,3,25.35,1,256.95 1263 | 316,2,25.35,1,210.92 1264 | 316,1,25.35,1,158.45 1265 | 316,0,25.35,1,96.7 1266 | 317,3,25.06,1,267.65 1267 | 317,2,25.06,1,187.52 1268 | 317,1,25.06,1,159.12 1269 | 317,0,25.06,0,37.84 1270 | 318,3,22.58,1,209.7 1271 | 318,2,22.58,1,127.65 1272 | 318,1,22.58,0,48.89 1273 | 318,0,22.58,0,10.05 1274 | 319,3,20.4,1,160.23 1275 | 319,2,20.4,0,64.35 1276 | 319,1,20.4,0,9.06 1277 | 319,0,20.4,0,0.0 1278 | 320,3,27.83,1,190.51 1279 | 320,2,27.83,1,144.31 1280 | 320,1,27.83,1,76.09 1281 | 320,0,27.83,1,25.86 1282 | 321,3,17.94,0,100.28 1283 | 321,2,17.94,0,73.04 1284 | 321,1,17.94,0,26.54 1285 | 321,0,17.94,0,13.71 1286 | 322,3,18.79,1,125.95 1287 | 322,2,18.79,1,108.02 1288 | 322,1,18.79,0,0.0 1289 | 322,0,18.79,0,0.0 1290 | 323,3,19.7,1,184.89 1291 | 323,2,19.7,0,109.54 1292 | 323,1,19.7,1,111.26 1293 | 323,0,19.7,1,75.53 1294 | 324,3,26.49,0,140.86 1295 | 324,2,26.49,1,123.88 1296 | 324,1,26.49,1,57.11 1297 | 324,0,26.49,1,36.83 1298 | 325,3,20.69,0,150.43 1299 | 325,2,20.69,0,128.67 1300 | 325,1,20.69,1,128.95 1301 | 325,0,20.69,0,41.12 1302 | 326,3,18.85,1,220.07 1303 | 326,2,18.85,1,206.68 1304 | 326,1,18.85,0,83.75 1305 | 326,0,18.85,0,60.03 1306 | 327,3,18.13,1,128.23 1307 | 327,2,18.13,1,101.57 1308 | 327,1,18.13,0,5.92 1309 | 327,0,18.13,0,0.0 1310 | 328,3,25.25,1,266.71 1311 | 328,2,25.25,1,231.15 1312 | 328,1,25.25,0,130.07 1313 | 328,0,25.25,1,115.52 1314 | 329,3,21.24,1,183.8 1315 | 329,2,21.24,1,127.39 1316 | 329,1,21.24,0,36.52 1317 | 329,0,21.24,0,0.0 1318 | 330,3,19.08,1,233.16 1319 | 330,2,19.08,0,140.11 1320 | 330,1,19.08,0,97.88 1321 | 330,0,19.08,0,67.71 1322 | 331,3,29.86,1,274.47 1323 | 331,2,29.86,1,213.48 1324 | 331,1,29.86,1,159.06 1325 | 331,0,29.86,1,90.95 1326 | 332,3,21.28,1,252.55 1327 | 332,2,21.28,1,202.2 1328 | 332,1,21.28,0,126.66 1329 | 332,0,21.28,0,68.74 1330 | 333,3,18.74,0,67.09 1331 | 333,2,18.74,1,82.39 1332 | 333,1,18.74,0,4.13 1333 | 333,0,18.74,0,0.0 1334 | 334,3,12.8,1,125.02 1335 | 334,2,12.8,0,66.1 1336 | 334,1,12.8,0,22.6 1337 | 334,0,12.8,0,0.0 1338 | 335,3,18.31,1,251.08 1339 | 335,2,18.31,1,206.23 1340 | 335,1,18.31,0,101.0 1341 | 335,0,18.31,0,69.6 1342 | 336,3,16.68,0,66.39 1343 | 336,2,16.68,0,58.17 1344 | 336,1,16.68,1,58.94 1345 | 336,0,16.68,0,0.0 1346 | 337,3,25.12,1,121.47 1347 | 337,2,25.12,1,65.25 1348 | 337,1,25.12,1,16.1 1349 | 337,0,25.12,1,0.0 1350 | 338,3,21.78,1,93.69 1351 | 338,2,21.78,1,55.31 1352 | 338,1,21.78,1,9.3 1353 | 338,0,21.78,0,0.0 1354 | 339,3,18.77,1,71.84 1355 | 339,2,18.77,0,0.0 1356 | 339,1,18.77,0,0.0 1357 | 339,0,18.77,0,0.0 1358 | 340,3,11.1,0,0.0 1359 | 340,2,11.1,0,0.0 1360 | 340,1,11.1,0,0.0 1361 | 340,0,11.1,0,0.0 1362 | 341,3,21.15,1,171.47 1363 | 341,2,21.15,1,136.47 1364 | 341,1,21.15,0,35.92 1365 | 341,0,21.15,0,0.0 1366 | 342,3,16.76,1,221.65 1367 | 342,2,16.76,0,126.66 1368 | 342,1,16.76,1,159.91 1369 | 342,0,16.76,0,57.79 1370 | 343,3,25.18,1,225.6 1371 | 343,2,25.18,1,138.89 1372 | 343,1,25.18,1,108.67 1373 | 343,0,25.18,1,27.71 1374 | 344,3,10.49,0,80.37 1375 | 344,2,10.49,1,115.77 1376 | 344,1,10.49,1,80.42 1377 | 344,0,10.49,1,64.6 1378 | 345,3,23.51,1,199.58 1379 | 345,2,23.51,1,147.27 1380 | 345,1,23.51,1,123.86 1381 | 345,0,23.51,1,70.27 1382 | 346,3,20.92,0,152.29 1383 | 346,2,20.92,1,139.62 1384 | 346,1,20.92,1,102.67 1385 | 346,0,20.92,0,17.71 1386 | 347,3,18.59,0,116.33 1387 | 347,2,18.59,1,123.65 1388 | 347,1,18.59,1,76.66 1389 | 347,0,18.59,0,3.55 1390 | 348,3,19.14,1,169.93 1391 | 348,2,19.14,0,67.15 1392 | 348,1,19.14,0,51.72 1393 | 348,0,19.14,1,53.48 1394 | 349,3,18.53,1,190.64 1395 | 349,2,18.53,1,147.92 1396 | 349,1,18.53,0,72.04 1397 | 349,0,18.53,0,41.13 1398 | 350,3,12.76,1,55.99 1399 | 350,2,12.76,0,0.0 1400 | 350,1,12.76,0,0.0 1401 | 350,0,12.76,0,0.0 1402 | 351,3,18.85,0,164.37 1403 | 351,2,18.85,1,195.1 1404 | 351,1,18.85,0,106.62 1405 | 351,0,18.85,0,71.72 1406 | 352,3,20.98,1,231.53 1407 | 352,2,20.98,1,192.28 1408 | 352,1,20.98,1,143.23 1409 | 352,0,20.98,0,49.18 1410 | 353,3,17.79,1,149.02 1411 | 353,2,17.79,1,120.36 1412 | 353,1,17.79,1,74.53 1413 | 353,0,17.79,0,11.52 1414 | 354,3,18.53,1,217.52 1415 | 354,2,18.53,1,185.24 1416 | 354,1,18.53,1,127.97 1417 | 354,0,18.53,0,48.81 1418 | 355,3,23.75,1,170.17 1419 | 355,2,23.75,1,126.64 1420 | 355,1,23.75,0,29.84 1421 | 355,0,23.75,1,24.21 1422 | 356,3,21.99,1,164.62 1423 | 356,2,21.99,1,108.77 1424 | 356,1,21.99,0,9.72 1425 | 356,0,21.99,0,0.0 1426 | 357,3,18.51,0,141.41 1427 | 357,2,18.51,0,86.31 1428 | 357,1,18.51,1,81.47 1429 | 357,0,18.51,1,55.59 1430 | 358,3,14.72,1,119.47 1431 | 358,2,14.72,0,56.78 1432 | 358,1,14.72,1,81.91 1433 | 358,0,14.72,0,0.0 1434 | 359,3,22.25,1,185.43 1435 | 359,2,22.25,1,125.05 1436 | 359,1,22.25,0,31.73 1437 | 359,0,22.25,0,0.0 1438 | 360,3,16.22,1,120.95 1439 | 360,2,16.22,0,37.0 1440 | 360,1,16.22,0,0.49 1441 | 360,0,16.22,0,0.0 1442 | 361,3,19.18,0,101.88 1443 | 361,2,19.18,1,102.51 1444 | 361,1,19.18,0,22.99 1445 | 361,0,19.18,0,0.0 1446 | 362,3,22.84,1,224.55 1447 | 362,2,22.84,1,208.0 1448 | 362,1,22.84,0,97.35 1449 | 362,0,22.84,0,52.46 1450 | 363,3,28.56,1,138.35 1451 | 363,2,28.56,0,44.3 1452 | 363,1,28.56,1,19.34 1453 | 363,0,28.56,1,0.0 1454 | 364,3,18.13,1,196.6 1455 | 364,2,18.13,1,152.79 1456 | 364,1,18.13,1,113.11 1457 | 364,0,18.13,0,15.45 1458 | 365,3,20.11,1,164.11 1459 | 365,2,20.11,0,77.79 1460 | 365,1,20.11,0,40.99 1461 | 365,0,20.11,0,5.74 1462 | 366,3,16.27,0,127.94 1463 | 366,2,16.27,0,113.51 1464 | 366,1,16.27,0,73.56 1465 | 366,0,16.27,0,49.59 1466 | 367,3,16.6,1,184.15 1467 | 367,2,16.6,1,131.85 1468 | 367,1,16.6,1,118.03 1469 | 367,0,16.6,1,86.05 1470 | 368,3,17.06,0,0.0 1471 | 368,2,17.06,0,0.0 1472 | 368,1,17.06,0,0.0 1473 | 368,0,17.06,0,0.0 1474 | 369,3,19.61,0,162.3 1475 | 369,2,19.61,1,173.06 1476 | 369,1,19.61,1,125.56 1477 | 369,0,19.61,1,97.8 1478 | 370,3,15.42,0,122.29 1479 | 370,2,15.42,0,91.88 1480 | 370,1,15.42,0,57.73 1481 | 370,0,15.42,1,70.37 1482 | 371,3,15.84,1,163.97 1483 | 371,2,15.84,1,114.83 1484 | 371,1,15.84,1,89.74 1485 | 371,0,15.84,0,29.14 1486 | 372,3,19.51,1,170.48 1487 | 372,2,19.51,1,105.86 1488 | 372,1,19.51,0,12.61 1489 | 372,0,19.51,1,30.93 1490 | 373,3,29.36,1,302.5 1491 | 373,2,29.36,1,226.4 1492 | 373,1,29.36,1,166.68 1493 | 373,0,29.36,1,93.29 1494 | 374,3,16.72,0,72.5 1495 | 374,2,16.72,0,48.96 1496 | 374,1,16.72,0,13.62 1497 | 374,0,16.72,0,4.88 1498 | 375,3,17.91,0,197.76 1499 | 375,2,17.91,0,173.32 1500 | 375,1,17.91,0,123.73 1501 | 375,0,17.91,0,89.39 1502 | 376,3,15.6,1,181.94 1503 | 376,2,15.6,0,90.3 1504 | 376,1,15.6,0,59.61 1505 | 376,0,15.6,0,46.86 1506 | 377,3,15.75,0,208.2 1507 | 377,2,15.75,0,190.15 1508 | 377,1,15.75,1,195.78 1509 | 377,0,15.75,0,118.63 1510 | 378,3,14.4,1,113.13 1511 | 378,2,14.4,1,68.29 1512 | 378,1,14.4,0,0.0 1513 | 378,0,14.4,0,0.0 1514 | 379,3,18.61,0,20.15 1515 | 379,2,18.61,1,52.16 1516 | 379,1,18.61,0,0.0 1517 | 379,0,18.61,1,0.0 1518 | 380,3,20.12,1,229.51 1519 | 380,2,20.12,1,180.05 1520 | 380,1,20.12,0,95.49 1521 | 380,0,20.12,1,90.04 1522 | 381,3,21.13,0,179.32 1523 | 381,2,21.13,1,184.13 1524 | 381,1,21.13,1,157.89 1525 | 381,0,21.13,0,70.76 1526 | 382,3,21.32,0,130.67 1527 | 382,2,21.32,1,152.55 1528 | 382,1,21.32,1,93.59 1529 | 382,0,21.32,0,13.34 1530 | 383,3,15.63,1,83.93 1531 | 383,2,15.63,1,20.5 1532 | 383,1,15.63,0,0.0 1533 | 383,0,15.63,0,0.0 1534 | 384,3,19.61,1,160.15 1535 | 384,2,19.61,1,105.22 1536 | 384,1,19.61,1,71.49 1537 | 384,0,19.61,0,0.0 1538 | 385,3,16.68,1,172.86 1539 | 385,2,16.68,1,132.61 1540 | 385,1,16.68,1,133.81 1541 | 385,0,16.68,0,27.7 1542 | 386,3,16.94,1,166.79 1543 | 386,2,16.94,1,101.96 1544 | 386,1,16.94,0,22.7 1545 | 386,0,16.94,1,51.23 1546 | 387,3,13.92,0,89.07 1547 | 387,2,13.92,1,103.77 1548 | 387,1,13.92,0,29.91 1549 | 387,0,13.92,0,0.0 1550 | 388,3,17.37,0,107.21 1551 | 388,2,17.37,1,104.78 1552 | 388,1,17.37,1,94.33 1553 | 388,0,17.37,0,0.0 1554 | 389,3,22.66,1,164.13 1555 | 389,2,22.66,1,105.45 1556 | 389,1,22.66,0,0.0 1557 | 389,0,22.66,0,0.0 1558 | 390,3,20.49,0,111.39 1559 | 390,2,20.49,1,122.31 1560 | 390,1,20.49,0,26.27 1561 | 390,0,20.49,0,0.0 1562 | 391,3,19.29,1,87.8 1563 | 391,2,19.29,0,27.69 1564 | 391,1,19.29,1,12.09 1565 | 391,0,19.29,1,0.0 1566 | 392,3,31.31,1,339.18 1567 | 392,2,31.31,1,295.7 1568 | 392,1,31.31,1,248.63 1569 | 392,0,31.31,1,153.88 1570 | 393,3,17.33,0,95.53 1571 | 393,2,17.33,0,53.53 1572 | 393,1,17.33,1,59.42 1573 | 393,0,17.33,0,0.0 1574 | 394,3,22.3,1,211.84 1575 | 394,2,22.3,0,115.67 1576 | 394,1,22.3,0,90.02 1577 | 394,0,22.3,1,90.4 1578 | 395,3,29.52,1,194.2 1579 | 395,2,29.52,1,127.68 1580 | 395,1,29.52,1,64.91 1581 | 395,0,29.52,1,30.08 1582 | 396,3,17.82,0,143.52 1583 | 396,2,17.82,0,116.36 1584 | 396,1,17.82,0,62.94 1585 | 396,0,17.82,1,91.38 1586 | 397,3,17.13,0,16.84 1587 | 397,2,17.13,0,0.0 1588 | 397,1,17.13,0,0.0 1589 | 397,0,17.13,0,0.0 1590 | 398,3,20.85,1,222.84 1591 | 398,2,20.85,1,181.32 1592 | 398,1,20.85,0,87.29 1593 | 398,0,20.85,1,102.08 1594 | 399,3,17.18,1,149.49 1595 | 399,2,17.18,1,146.72 1596 | 399,1,17.18,0,49.3 1597 | 399,0,17.18,0,11.21 1598 | 400,3,23.81,1,320.53 1599 | 400,2,23.81,1,259.29 1600 | 400,1,23.81,1,206.68 1601 | 400,0,23.81,1,179.82 1602 | 401,3,20.22,1,86.28 1603 | 401,2,20.22,1,81.65 1604 | 401,1,20.22,1,51.47 1605 | 401,0,20.22,0,0.0 1606 | 402,3,21.12,0,140.01 1607 | 402,2,21.12,0,95.32 1608 | 402,1,21.12,0,56.0 1609 | 402,0,21.12,1,49.12 1610 | 403,3,20.86,0,149.53 1611 | 403,2,20.86,1,164.84 1612 | 403,1,20.86,1,120.23 1613 | 403,0,20.86,0,28.82 1614 | 404,3,19.19,0,174.78 1615 | 404,2,19.19,0,155.09 1616 | 404,1,19.19,0,121.25 1617 | 404,0,19.19,1,87.73 1618 | 405,3,24.12,1,195.21 1619 | 405,2,24.12,1,119.48 1620 | 405,1,24.12,1,89.29 1621 | 405,0,24.12,1,36.7 1622 | 406,3,24.95,1,239.01 1623 | 406,2,24.95,1,184.64 1624 | 406,1,24.95,1,103.12 1625 | 406,0,24.95,0,11.39 1626 | 407,3,25.24,1,129.86 1627 | 407,2,25.24,1,45.87 1628 | 407,1,25.24,1,25.28 1629 | 407,0,25.24,1,0.0 1630 | 408,3,26.08,1,188.18 1631 | 408,2,26.08,1,117.59 1632 | 408,1,26.08,1,67.84 1633 | 408,0,26.08,0,0.0 1634 | 409,3,19.77,1,193.79 1635 | 409,2,19.77,0,106.67 1636 | 409,1,19.77,0,69.23 1637 | 409,0,19.77,1,95.38 1638 | 410,3,22.1,1,246.08 1639 | 410,2,22.1,1,202.54 1640 | 410,1,22.1,1,155.49 1641 | 410,0,22.1,1,109.05 1642 | 411,3,13.52,0,77.49 1643 | 411,2,13.52,0,38.73 1644 | 411,1,13.52,1,70.24 1645 | 411,0,13.52,0,0.0 1646 | 412,3,20.61,0,240.39 1647 | 412,2,20.61,1,236.97 1648 | 412,1,20.61,1,195.5 1649 | 412,0,20.61,0,114.62 1650 | 413,3,16.61,1,251.4 1651 | 413,2,16.61,0,132.49 1652 | 413,1,16.61,0,149.79 1653 | 413,0,16.61,0,88.53 1654 | 414,3,29.19,1,246.75 1655 | 414,2,29.19,1,191.86 1656 | 414,1,29.19,1,138.71 1657 | 414,0,29.19,1,75.72 1658 | 415,3,23.27,0,20.79 1659 | 415,2,23.27,1,35.45 1660 | 415,1,23.27,0,0.0 1661 | 415,0,23.27,1,0.0 1662 | 416,3,14.56,0,8.08 1663 | 416,2,14.56,0,0.0 1664 | 416,1,14.56,0,0.0 1665 | 416,0,14.56,0,0.0 1666 | 417,3,28.54,1,200.75 1667 | 417,2,28.54,1,143.68 1668 | 417,1,28.54,1,98.71 1669 | 417,0,28.54,0,0.0 1670 | 418,3,15.47,1,204.5 1671 | 418,2,15.47,0,133.84 1672 | 418,1,15.47,1,132.84 1673 | 418,0,15.47,1,107.61 1674 | 419,3,22.29,1,210.14 1675 | 419,2,22.29,0,122.86 1676 | 419,1,22.29,1,113.4 1677 | 419,0,22.29,1,91.91 1678 | 420,3,27.12,1,132.94 1679 | 420,2,27.12,1,61.23 1680 | 420,1,27.12,1,25.51 1681 | 420,0,27.12,1,0.0 1682 | 421,3,17.21,0,92.37 1683 | 421,2,17.21,1,107.49 1684 | 421,1,17.21,0,20.08 1685 | 421,0,17.21,0,0.0 1686 | 422,3,27.08,0,278.85 1687 | 422,2,27.08,1,248.64 1688 | 422,1,27.08,1,207.95 1689 | 422,0,27.08,1,155.25 1690 | 423,3,28.05,1,215.98 1691 | 423,2,28.05,1,160.48 1692 | 423,1,28.05,0,51.0 1693 | 423,0,28.05,1,46.52 1694 | 424,3,19.65,0,86.24 1695 | 424,2,19.65,0,48.34 1696 | 424,1,19.65,0,0.0 1697 | 424,0,19.65,0,0.0 1698 | 425,3,19.4,0,229.35 1699 | 425,2,19.4,0,175.06 1700 | 425,1,19.4,1,207.05 1701 | 425,0,19.4,1,155.27 1702 | 426,3,21.16,0,132.12 1703 | 426,2,21.16,1,144.51 1704 | 426,1,21.16,0,49.69 1705 | 426,0,21.16,0,20.43 1706 | 427,3,18.4,0,104.17 1707 | 427,2,18.4,1,114.26 1708 | 427,1,18.4,0,34.79 1709 | 427,0,18.4,0,0.0 1710 | 428,3,19.89,1,170.91 1711 | 428,2,19.89,0,95.3 1712 | 428,1,19.89,0,62.97 1713 | 428,0,19.89,0,28.15 1714 | 429,3,20.66,0,134.49 1715 | 429,2,20.66,0,89.91 1716 | 429,1,20.66,1,101.4 1717 | 429,0,20.66,1,71.47 1718 | 430,3,18.31,1,155.84 1719 | 430,2,18.31,0,65.33 1720 | 430,1,18.31,0,15.1 1721 | 430,0,18.31,0,0.0 1722 | 431,3,20.12,1,248.05 1723 | 431,2,20.12,0,162.69 1724 | 431,1,20.12,0,123.41 1725 | 431,0,20.12,0,80.26 1726 | 432,3,27.78,1,291.04 1727 | 432,2,27.78,1,233.78 1728 | 432,1,27.78,0,114.92 1729 | 432,0,27.78,1,124.32 1730 | 433,3,25.25,1,243.8 1731 | 433,2,25.25,1,215.52 1732 | 433,1,25.25,0,111.75 1733 | 433,0,25.25,1,100.49 1734 | 434,3,23.88,0,154.99 1735 | 434,2,23.88,1,160.46 1736 | 434,1,23.88,1,107.53 1737 | 434,0,23.88,0,14.93 1738 | 435,3,28.92,1,293.69 1739 | 435,2,28.92,1,242.32 1740 | 435,1,28.92,1,204.16 1741 | 435,0,28.92,1,128.61 1742 | 436,3,21.88,0,156.31 1743 | 436,2,21.88,0,120.43 1744 | 436,1,21.88,1,93.95 1745 | 436,0,21.88,0,26.27 1746 | 437,3,12.45,0,82.89 1747 | 437,2,12.45,1,139.56 1748 | 437,1,12.45,0,43.59 1749 | 437,0,12.45,0,17.84 1750 | 438,3,19.81,0,202.66 1751 | 438,2,19.81,1,191.87 1752 | 438,1,19.81,0,99.76 1753 | 438,0,19.81,0,43.58 1754 | 439,3,14.53,0,120.83 1755 | 439,2,14.53,1,153.42 1756 | 439,1,14.53,0,81.31 1757 | 439,0,14.53,0,62.72 1758 | 440,3,22.78,1,274.88 1759 | 440,2,22.78,1,256.53 1760 | 440,1,22.78,0,135.93 1761 | 440,0,22.78,0,108.78 1762 | 441,3,19.53,1,183.39 1763 | 441,2,19.53,1,147.84 1764 | 441,1,19.53,0,66.24 1765 | 441,0,19.53,0,46.93 1766 | 442,3,21.62,1,98.21 1767 | 442,2,21.62,0,8.1 1768 | 442,1,21.62,0,0.0 1769 | 442,0,21.62,1,0.0 1770 | 443,3,18.12,0,59.49 1771 | 443,2,18.12,0,22.99 1772 | 443,1,18.12,0,0.0 1773 | 443,0,18.12,0,0.0 1774 | 444,3,21.18,1,252.21 1775 | 444,2,21.18,0,173.89 1776 | 444,1,21.18,1,165.86 1777 | 444,0,21.18,0,97.83 1778 | 445,3,11.96,1,99.44 1779 | 445,2,11.96,1,85.49 1780 | 445,1,11.96,1,50.04 1781 | 445,0,11.96,0,0.0 1782 | 446,3,17.94,0,134.9 1783 | 446,2,17.94,0,104.36 1784 | 446,1,17.94,0,63.58 1785 | 446,0,17.94,1,79.76 1786 | 447,3,13.82,0,19.19 1787 | 447,2,13.82,0,16.32 1788 | 447,1,13.82,0,0.0 1789 | 447,0,13.82,0,0.0 1790 | 448,3,15.68,1,194.37 1791 | 448,2,15.68,0,121.62 1792 | 448,1,15.68,1,144.85 1793 | 448,0,15.68,0,64.82 1794 | 449,3,25.58,1,198.1 1795 | 449,2,25.58,1,146.45 1796 | 449,1,25.58,0,45.35 1797 | 449,0,25.58,1,39.77 1798 | 450,3,14.64,1,234.24 1799 | 450,2,14.64,1,160.04 1800 | 450,1,14.64,0,113.03 1801 | 450,0,14.64,0,74.85 1802 | 451,3,26.89,0,164.36 1803 | 451,2,26.89,1,154.66 1804 | 451,1,26.89,1,96.77 1805 | 451,0,26.89,0,0.0 1806 | 452,3,20.34,1,241.89 1807 | 452,2,20.34,1,217.39 1808 | 452,1,20.34,0,87.11 1809 | 452,0,20.34,0,60.73 1810 | 453,3,22.36,1,222.59 1811 | 453,2,22.36,0,138.45 1812 | 453,1,22.36,0,92.45 1813 | 453,0,22.36,1,95.14 1814 | 454,3,23.58,1,174.53 1815 | 454,2,23.58,1,116.61 1816 | 454,1,23.58,0,37.55 1817 | 454,0,23.58,0,0.0 1818 | 455,3,23.57,1,272.74 1819 | 455,2,23.57,1,207.34 1820 | 455,1,23.57,0,124.19 1821 | 455,0,23.57,0,69.2 1822 | 456,3,22.76,1,154.77 1823 | 456,2,22.76,1,99.42 1824 | 456,1,22.76,1,77.06 1825 | 456,0,22.76,0,0.0 1826 | 457,3,14.41,1,106.36 1827 | 457,2,14.41,0,37.28 1828 | 457,1,14.41,0,0.0 1829 | 457,0,14.41,0,0.0 1830 | 458,3,26.52,1,279.9 1831 | 458,2,26.52,1,228.89 1832 | 458,1,26.52,1,154.07 1833 | 458,0,26.52,1,106.97 1834 | 459,3,17.17,0,66.74 1835 | 459,2,17.17,1,94.32 1836 | 459,1,17.17,0,10.51 1837 | 459,0,17.17,0,0.0 1838 | 460,3,21.11,0,116.23 1839 | 460,2,21.11,1,106.87 1840 | 460,1,21.11,1,73.32 1841 | 460,0,21.11,1,50.13 1842 | 461,3,17.83,1,152.84 1843 | 461,2,17.83,1,109.42 1844 | 461,1,17.83,0,36.21 1845 | 461,0,17.83,0,0.0 1846 | 462,3,21.06,1,144.64 1847 | 462,2,21.06,0,43.2 1848 | 462,1,21.06,1,49.17 1849 | 462,0,21.06,0,0.0 1850 | 463,3,19.97,1,224.46 1851 | 463,2,19.97,0,139.51 1852 | 463,1,19.97,0,109.77 1853 | 463,0,19.97,1,113.0 1854 | 464,3,24.42,0,204.95 1855 | 464,2,24.42,1,171.26 1856 | 464,1,24.42,1,154.56 1857 | 464,0,24.42,1,101.02 1858 | 465,3,17.01,1,142.6 1859 | 465,2,17.01,1,133.57 1860 | 465,1,17.01,1,80.96 1861 | 465,0,17.01,0,10.42 1862 | 466,3,23.22,1,189.36 1863 | 466,2,23.22,1,137.97 1864 | 466,1,23.22,1,90.3 1865 | 466,0,23.22,0,0.0 1866 | 467,3,19.35,1,201.3 1867 | 467,2,19.35,0,117.69 1868 | 467,1,19.35,1,105.0 1869 | 467,0,19.35,0,27.1 1870 | 468,3,19.82,1,277.81 1871 | 468,2,19.82,1,214.71 1872 | 468,1,19.82,1,183.64 1873 | 468,0,19.82,0,110.94 1874 | 469,3,19.7,1,223.48 1875 | 469,2,19.7,0,124.78 1876 | 469,1,19.7,0,76.12 1877 | 469,0,19.7,1,114.4 1878 | 470,3,18.25,1,156.69 1879 | 470,2,18.25,0,39.03 1880 | 470,1,18.25,1,87.46 1881 | 470,0,18.25,0,0.0 1882 | 471,3,35.06,1,339.32 1883 | 471,2,35.06,1,257.95 1884 | 471,1,35.06,1,180.2 1885 | 471,0,35.06,1,119.58 1886 | 472,3,16.35,1,143.73 1887 | 472,2,16.35,1,107.06 1888 | 472,1,16.35,0,20.4 1889 | 472,0,16.35,0,0.0 1890 | 473,3,16.68,1,166.32 1891 | 473,2,16.68,1,124.73 1892 | 473,1,16.68,0,17.37 1893 | 473,0,16.68,1,25.52 1894 | 474,3,28.14,0,283.35 1895 | 474,2,28.14,1,285.66 1896 | 474,1,28.14,1,220.24 1897 | 474,0,28.14,1,172.49 1898 | 475,3,20.74,0,112.57 1899 | 475,2,20.74,1,121.73 1900 | 475,1,20.74,0,39.26 1901 | 475,0,20.74,0,0.0 1902 | 476,3,14.41,0,70.87 1903 | 476,2,14.41,0,22.08 1904 | 476,1,14.41,0,19.42 1905 | 476,0,14.41,0,0.0 1906 | 477,3,17.53,1,52.91 1907 | 477,2,17.53,0,0.0 1908 | 477,1,17.53,0,0.0 1909 | 477,0,17.53,0,0.0 1910 | 478,3,17.02,0,54.72 1911 | 478,2,17.02,1,43.48 1912 | 478,1,17.02,0,0.0 1913 | 478,0,17.02,0,0.0 1914 | 479,3,23.29,0,178.0 1915 | 479,2,23.29,1,190.13 1916 | 479,1,23.29,1,132.78 1917 | 479,0,23.29,1,79.85 1918 | 480,3,20.03,0,108.09 1919 | 480,2,20.03,1,109.56 1920 | 480,1,20.03,0,32.8 1921 | 480,0,20.03,0,0.0 1922 | 481,3,24.02,0,186.98 1923 | 481,2,24.02,1,198.67 1924 | 481,1,24.02,1,147.09 1925 | 481,0,24.02,0,41.3 1926 | 482,3,19.58,0,66.79 1927 | 482,2,19.58,1,82.64 1928 | 482,1,19.58,0,4.93 1929 | 482,0,19.58,1,0.0 1930 | 483,3,20.89,0,101.56 1931 | 483,2,20.89,0,61.12 1932 | 483,1,20.89,0,5.44 1933 | 483,0,20.89,1,34.2 1934 | 484,3,22.07,1,220.41 1935 | 484,2,22.07,0,144.69 1936 | 484,1,22.07,0,103.47 1937 | 484,0,22.07,1,96.81 1938 | 485,3,19.93,0,196.48 1939 | 485,2,19.93,0,168.66 1940 | 485,1,19.93,1,183.57 1941 | 485,0,19.93,1,138.27 1942 | 486,3,27.87,1,250.88 1943 | 486,2,27.87,1,188.79 1944 | 486,1,27.87,1,171.95 1945 | 486,0,27.87,1,87.12 1946 | 487,3,14.35,0,138.44 1947 | 487,2,14.35,1,157.43 1948 | 487,1,14.35,0,90.05 1949 | 487,0,14.35,0,53.69 1950 | 488,3,22.0,1,263.38 1951 | 488,2,22.0,1,237.85 1952 | 488,1,22.0,1,186.41 1953 | 488,0,22.0,0,100.49 1954 | 489,3,19.15,0,162.0 1955 | 489,2,19.15,1,193.8 1956 | 489,1,19.15,0,107.19 1957 | 489,0,19.15,0,65.92 1958 | 490,3,11.56,0,22.07 1959 | 490,2,11.56,1,46.99 1960 | 490,1,11.56,0,0.0 1961 | 490,0,11.56,0,0.0 1962 | 491,3,21.53,0,113.71 1963 | 491,2,21.53,0,87.36 1964 | 491,1,21.53,0,56.77 1965 | 491,0,21.53,1,39.11 1966 | 492,3,16.62,1,181.47 1967 | 492,2,16.62,1,154.62 1968 | 492,1,16.62,0,82.8 1969 | 492,0,16.62,0,37.44 1970 | 493,3,29.84,1,214.83 1971 | 493,2,29.84,1,153.51 1972 | 493,1,29.84,1,109.54 1973 | 493,0,29.84,0,5.18 1974 | 494,3,14.96,0,116.53 1975 | 494,2,14.96,1,136.5 1976 | 494,1,14.96,0,75.26 1977 | 494,0,14.96,0,24.5 1978 | 495,3,27.22,1,166.75 1979 | 495,2,27.22,1,98.28 1980 | 495,1,27.22,0,0.0 1981 | 495,0,27.22,1,0.0 1982 | 496,3,34.31,1,226.26 1983 | 496,2,34.31,1,167.81 1984 | 496,1,34.31,1,95.04 1985 | 496,0,34.31,1,31.69 1986 | 497,3,15.19,1,156.64 1987 | 497,2,15.19,1,143.27 1988 | 497,1,15.19,1,105.14 1989 | 497,0,15.19,0,34.54 1990 | 498,3,26.06,1,111.42 1991 | 498,2,26.06,1,76.5 1992 | 498,1,26.06,1,16.11 1993 | 498,0,26.06,0,0.0 1994 | 499,3,23.1,0,100.94 1995 | 499,2,23.1,0,47.24 1996 | 499,1,23.1,0,2.33 1997 | 499,0,23.1,1,15.6 1998 | 500,3,20.52,0,154.68 1999 | 500,2,20.52,0,93.52 2000 | 500,1,20.52,1,111.16 2001 | 500,0,20.52,0,3.77 2002 | --------------------------------------------------------------------------------