├── README.md ├── .gitignore ├── ProjectProposal_Group028_WI24.ipynb ├── template_FinalProject_Group028_WI24.ipynb └── assets └── CPIAUCSL.csv /README.md: -------------------------------------------------------------------------------- 1 | This is your group repo for your final project for COGS108. 2 | 3 | This repository is private, and is only visible to the course instructors and your group mates; it is not visible to anyone else. 4 | 5 | Template notebooks for each component are provided. Only work on the notebook prior to its due date. After each submission is due, move onto the next notebook (For example, after the proposal is due, start working in the Data Checkpoint notebook). 6 | 7 | This repository will be frozen on the final project due date. No further changes can be made after that time. 8 | 9 | Your project proposal and final project will be graded based solely on the corresponding project notebooks in this repository. 10 | 11 | Template Jupyter notebooks have been included, with your group number replacing the XXX in the following file names. For each due date, make sure you have a notebook present in this repository by each due date with the following name (where XXX is replaced by your group number): 12 | 13 | - `ProjectProposal_groupXXX.ipynb` 14 | - `DataCheckpoint_groupXXX.ipynb` 15 | - `EDACheckpoint_groupXXX.ipynb` 16 | - `FinalProject_groupXXX.ipynb` 17 | 18 | This is *your* repo. You are free to manage the repo as you see fit, edit this README, add data files, add scripts, etc. So long as there are the four files above on due dates with the required information, the rest is up to you all. 19 | 20 | Also, you are free and encouraged to share this project after the course and to add it to your portfolio. Just be sure to fork it to your GitHub at the end of the quarter! 21 | -------------------------------------------------------------------------------- /.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 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 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 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | 162 | -------------------------------------------------------------------------------- /ProjectProposal_Group028_WI24.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# COGS 108 - Project Proposal" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Names\n", 15 | "\n", 16 | "- James DeLisio\n", 17 | "- Karisma Kumar\n", 18 | "- Chi Kiet Chou\n", 19 | "- Anthony Gonzalez\n", 20 | "- Chengyuan Mao" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "# Research Question" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "Our research question sets out to examine what characteristics of movies influence their user rating score on IMDB. In particular, we will look at the runtime, budget, genre, MPA (Motion Picture Association) Rating, and release month to see if any of these metrics correlate with movie’s average user rating score on IMDB. In addition, we want to use revenue as a secondary baseline to understand the significance of the characteristics. Furthermore, we are curious about the relationship between revenue made by the movies and its user ratings.\n", 35 | "\n" 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "## Background and Prior Work" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "\n", 50 | "Movies are one of the oldest and most influential forms of entertainment. As shown throughout history, movies have pushed technology advancement over and over again. Today, the cinema industry has grown to one of the most competitive and expensive industries. Many corporations from all scales rely on successful movies to drive their comercial revenue. Thus, bringing up the significance of movie ratings. A secondary dependent variable we would like to consider as well is the revenue of a film. A profitable movie does not always make it a critical hit, and vice versa. However, from the studio’s perspective, a lucrative film is a success. As such, we are also interested in seeing how the prior factors correlate to a film’s overall revenue.\n", 51 | "\n", 52 | "Although being a competitive industry, we have seen time and time where indie movies with low budgets shocked the media with positive ratings[1](#cite_note-1). On the other hand, many movies from big corporations have historically been crowned as the biggest flops in both critics and audience ratings, despite having triple or quadruple budgets[2](#cite_note-2). Despite the outliers, as shown in “A predictor for movie success”[3](#cite_note-3), the budget has been pretty consistent with the movie’s revenue which could indicate that their ratings could be positively correlated.\n", 53 | "\n", 54 | "Having backed a bigger fund would allow big studios to be more flexible in their runtime. However, the longer the runtime is the higher the risk of discomfort and undesired experience, as evidently that human’s attention span is limited[4](#cite_note-4). Meanwhile, shorter movies limited the movies’ content and how it is delivered. All of which could impact the movie ratings. Finally, due to the long history of movies, some would regard movies as a medium of cultural exchange. It isn’t surprising that movie studios make very intentional choices when it comes to the genre of the story and the maturity of the content in the movie, as they all have different audiences, standards, and competition. Furthermore, we found a prior study about the release date’s impact on the ratings[1](#cite_note-1). Although they didn’t directly show any significance of release date in correlation with ratings, it is also traditional for movies with certain content to be released on a certain date such as October having the highest number of horror movie releases[5](#cite_note-5).\n", 55 | "\n", 56 | "1. [^](#cite_ref-1) Gama, D. (2023). The 15 Best Indie Movies of All Time, According to Reddit https://collider.com/best-indie-movies-reddit \n", 57 | "2. [^](#cite_ref-2) Ryan, J. (2021). Biggest movie flops: The 42 biggest box-office bombs https://www.cbsnews.com/pictures/biggest-movie-flops-box-office-bombs/ \n", 58 | "3. [^](#cite_ref-3) Ericson, J., & Goodman, J. (2013). A predictor for movie success. CS229, Stanford University. https://cs229.stanford.edu/proj2013/EricsonGrodman-APredictorForMovieSuccess.pdf \n", 59 | "4. [^](#cite_ref-4) Hollander, A. (2023). Average Human Attention Span By Age: 60 Statistics https://www.bridgecareaba.com/blog/average-human-attention-span \n", 60 | "5. [^](#cite_ref-5) https://wheresthejump.com/monthly-distribution-of-horror-movies-during-what-period-of-the-year-are-most-horror-movies-released/ \n" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "# Hypothesis\n" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "\n", 75 | "We predict that of all of our listed factors, the budget and MPA rating of a film will be most strongly correlated with the IMDB average audience rating, with higher budgets and “R” ratings correlating with higher audience ratings. We predict that longer runtimes, being a drama, and being released during the summer will also correlate with a higher average rating on IMDB, since we imagine longer dramas released during summer blockbuster season are more likely to perform well with critics. Similarly, we expect the correlation of each factor will remain the same when put against revenue. In addition, we predict that revenue has a positive correlation with the average user ratings." 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "# Data" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "For our ideal dataset, we would need information on several metrics regarding films pulled from IMDB’s movie database. We would need the film’s average user rating, the runtime of the film, its budget (in USD), its total box office revenue (in USD), release month, genre, and MPA rating (G, PG, PG-13, R, etc). We should note that our monetary metrics (budget and revenue) should all be adjusted for inflation to make for a valid comparison. \n", 90 | "Previous sources have mentioned the difficulty of scraping data from IMDB due to its inconsistent data entry formats (data is stored in text documents, not in tables or .csv files). If possible, we hope to avoid scraping this data directly from IMDB and will instead seek out recent existing databases that contain information from IMDB. We will also be limiting our dataset to only include movies, although IMDB also includes television series in its database." 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "# Ethics & Privacy" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "Throughout our research project, we aim to keep ethical practice and user privacy as top priorities in our process. All data we plan to use will be publicly available. A film’s revenue and information about its characteristics are all publicly available and impersonal information, and the IMDB average rating for a film is a mean taken from several anonymous individual reviewers, meaning our variables of interest are all ethically sourced. Our datasets will likely mostly be taken from Kaggle, which hosts publicly available data, and we plan to take full responsibility for the ethical implications of our results. \n", 105 | "\n", 106 | "While movies are a fairly casual and broad topic, we were careful in selecting our variables to consider any analysis that may be sensitive or allow room for misleading results or interpretations. While we are accessing user’s ratings of a film on IMDB, we have no access to user’s personal information, and are instead pulling an average rating from the scores of countless users, meaning the privacy of IMDB users is protected. We are committed to analyzing our data and interpreting our results in an objective and unbiased manner, and will strive to avoid misrepresenting our results or engaging in any faulty analytical practices (p-hacking, etc.). By choosing our data carefully and dedicating ourselves to fair research practices, we hope to maintain ethics and privacy in our project." 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "# Team Expectations " 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": {}, 119 | "source": [ 120 | "\n", 121 | "Our team will communicate through a discord server that we are all in. We will be able to chat continuously on the server, and have meetings on voice call or in person when necessary. We expect that each team member will make consistent and significant contributions to the project, from research, to data collection, to coding, and writing up the final report. We will divide up the work on each phase of the project as it comes to make sure that nobody takes on more work than is necessary, and we expect that all team members will communicate early if they have other responsibilities or anything urgent comes up." 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "# Project Timeline Proposal" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "\n", 136 | "\n", 137 | "\n", 138 | "| Meeting Date | Meeting Time| Completed Before Meeting | Discuss at Meeting |\n", 139 | "|---|---|---|---|\n", 140 | "| Week 5 | 2 PM | Create discord | Discussed project topics, searched for datasets, discussed potential variables | \n", 141 | "| Week 7 | TBA | Project proposal, finding datasets | Discuss EDA approach | \n", 142 | "| Week 8 | TBA | Import data, wrangle data, perform EDA | Discuss analysis approach, divide up work |\n", 143 | "| Week 10 | TBA | Perform analysis and create visualizations | Discuss and interpret results, begin write-up |\n", 144 | "| Finals Week | TBA | Finish project write-up | Turn in Final Project & Group Project Surveys |" 145 | ] 146 | } 147 | ], 148 | "metadata": { 149 | "kernelspec": { 150 | "display_name": "Python 3 (ipykernel)", 151 | "language": "python", 152 | "name": "python3" 153 | }, 154 | "language_info": { 155 | "codemirror_mode": { 156 | "name": "ipython", 157 | "version": 3 158 | }, 159 | "file_extension": ".py", 160 | "mimetype": "text/x-python", 161 | "name": "python", 162 | "nbconvert_exporter": "python", 163 | "pygments_lexer": "ipython3", 164 | "version": "3.9.7" 165 | } 166 | }, 167 | "nbformat": 4, 168 | "nbformat_minor": 2 169 | } 170 | -------------------------------------------------------------------------------- /template_FinalProject_Group028_WI24.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# COGS 108 - Final Project (change this to your project's title)\n", 8 | "\n", 9 | "# Permissions\n", 10 | "\n", 11 | "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n", 12 | "\n", 13 | "* [ ] YES - make available\n", 14 | "* [ ] NO - keep private\n", 15 | "\n", 16 | "# Names\n", 17 | "\n", 18 | "- Ant Man\n", 19 | "- Hulk\n", 20 | "- Iron Man\n", 21 | "- Thor\n", 22 | "- Wasp\n", 23 | "\n", 24 | "# Abstract\n", 25 | "\n", 26 | "Please write one to four paragraphs that describe a very brief overview of why you did this, how you did, and the major findings and conclusions." 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "# Research Question" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "- Include a specific, clear data science question.\n", 41 | "- Make sure what you're measuring (variables) to answer the question is clear\n", 42 | "\n", 43 | "What is your research question? Include the specific question you're setting out to answer. This question should be specific, answerable with data, and clear. A general question with specific subquestions is permitted. (1-2 sentences)\n", 44 | "\n" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "## Background and Prior Work" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "\n", 59 | "- Include a general introduction to your topic\n", 60 | "- Include explanation of what work has been done previously\n", 61 | "- Include citations or links to previous work\n", 62 | "\n", 63 | "This section will present the background and context of your topic and question in a few paragraphs. Include a general introduction to your topic and then describe what information you currently know about the topic after doing your initial research. Include references to other projects who have asked similar questions or approached similar problems. Explain what others have learned in their projects.\n", 64 | "\n", 65 | "Find some relevant prior work, and reference those sources, summarizing what each did and what they learned. Even if you think you have a totally novel question, find the most similar prior work that you can and discuss how it relates to your project.\n", 66 | "\n", 67 | "References can be research publications, but they need not be. Blogs, GitHub repositories, company websites, etc., are all viable references if they are relevant to your project. It must be clear which information comes from which references. (2-3 paragraphs, including at least 2 references)\n", 68 | "\n", 69 | " **Use inline citation through HTML footnotes to specify which references support which statements** \n", 70 | "\n", 71 | "For example: After government genocide in the 20th century, real birds were replaced with surveillance drones designed to look just like birds.[1](#cite_note-1) Use a minimum of 2 or 3 citations, but we prefer more.[2](#cite_note-2) You need enough to fully explain and back up important facts. \n", 72 | "\n", 73 | "Note that if you click a footnote number in the paragraph above it will transport you to the proper entry in the footnotes list below. And if you click the ^ in the footnote entry, it will return you to the place in the main text where the footnote is made.\n", 74 | "\n", 75 | "To understand the HTML here, ` ` is a tag that allows you produce a named reference for a given location. Markdown has the construciton `[text with hyperlink](#named reference)` that will produce a clickable link that transports you the named reference.\n", 76 | "\n", 77 | "1. [^](#cite_ref-1) Lorenz, T. (9 Dec 2021) Birds Aren’t Real, or Are They? Inside a Gen Z Conspiracy Theory. *The New York Times*. https://www.nytimes.com/2021/12/09/technology/birds-arent-real-gen-z-misinformation.html \n", 78 | "2. [^](#cite_ref-2) Also refs should be important to the background, not some randomly chosen vaguely related stuff. Include a web link if possible in refs as above.\n" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "# Hypothesis\n" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "\n", 93 | "- Include your team's hypothesis\n", 94 | "- Ensure that this hypothesis is clear to readers\n", 95 | "- Explain why you think this will be the outcome (what was your thinking?)\n", 96 | "\n", 97 | "What is your main hypothesis/predictions about what the answer to your question is? Briefly explain your thinking. (2-3 sentences)" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "# Data" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "## Data overview\n", 112 | "\n", 113 | "For each dataset include the following information\n", 114 | "- Dataset #1\n", 115 | " - Dataset Name:\n", 116 | " - Link to the dataset:\n", 117 | " - Number of observations:\n", 118 | " - Number of variables:\n", 119 | "- Dataset #2 (if you have more than one!)\n", 120 | " - Dataset Name:\n", 121 | " - Link to the dataset:\n", 122 | " - Number of observations:\n", 123 | " - Number of variables:\n", 124 | "- etc\n", 125 | "\n", 126 | "Now write 2 - 5 sentences describing each dataset here. Include a short description of the important variables in the dataset; what the metrics and datatypes are, what concepts they may be proxies for. Include information about how you would need to wrangle/clean/preprocess the dataset\n", 127 | "\n", 128 | "If you plan to use multiple datasets, add a few sentences about how you plan to combine these datasets." 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "## Dataset #1 (use name instead of number here)" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": null, 141 | "metadata": {}, 142 | "outputs": [], 143 | "source": [ 144 | "## YOUR CODE TO LOAD/CLEAN/TIDY/WRANGLE THE DATA GOES HERE\n", 145 | "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION " 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "## Dataset #2 (if you have more than one, use name instead of number here)" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": null, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "## YOUR CODE TO LOAD/CLEAN/TIDY/WRANGLE THE DATA GOES HERE\n", 162 | "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION " 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "# Results\n", 170 | "\n", 171 | "## Exploratory Data Analysis\n", 172 | "\n", 173 | "Carry out whatever EDA you need to for your project. Because every project will be different we can't really give you much of a template at this point. But please make sure you describe the what and why in text here as well as providing interpretation of results and context." 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "## First Analysis You Did - Give it a better title\n", 181 | "\n", 182 | "Some more words and stuff. Remember notebooks work best if you interleave the code that generates a result with properly annotate figures and text that puts these results into context." 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": null, 188 | "metadata": {}, 189 | "outputs": [], 190 | "source": [ 191 | "## YOUR CODE HERE\n", 192 | "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION" 193 | ] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "metadata": {}, 198 | "source": [ 199 | "## Second Analysis You Did - Give it a better title\n", 200 | "\n", 201 | "Some more words and stuff. Remember notebooks work best if you interleave the code that generates a result with properly annotate figures and text that puts these results into context." 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "metadata": {}, 208 | "outputs": [], 209 | "source": [ 210 | "## YOUR CODE HERE\n", 211 | "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION" 212 | ] 213 | }, 214 | { 215 | "cell_type": "markdown", 216 | "metadata": {}, 217 | "source": [ 218 | "## ETC AD NASEUM\n", 219 | "\n", 220 | "Some more words and stuff. Remember notebooks work best if you interleave the code that generates a result with properly annotate figures and text that puts these results into context." 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "## YOUR CODE HERE\n", 230 | "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION" 231 | ] 232 | }, 233 | { 234 | "cell_type": "markdown", 235 | "metadata": {}, 236 | "source": [ 237 | "# Ethics & Privacy" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | "- Thoughtful discussion of ethical concerns included\n", 245 | "- Ethical concerns consider the whole data science process (question asked, data collected, data being used, the bias in data, analysis, post-analysis, etc.)\n", 246 | "- How your group handled bias/ethical concerns clearly described\n", 247 | "\n", 248 | "Acknowledge and address any ethics & privacy related issues of your question(s), proposed dataset(s), and/or analyses. Use the information provided in lecture to guide your group discussion and thinking. If you need further guidance, check out [Deon's Ethics Checklist](http://deon.drivendata.org/#data-science-ethics-checklist). In particular:\n", 249 | "\n", 250 | "- Are there any biases/privacy/terms of use issues with the data you propsed?\n", 251 | "- Are there potential biases in your dataset(s), in terms of who it composes, and how it was collected, that may be problematic in terms of it allowing for equitable analysis? (For example, does your data exclude particular populations, or is it likely to reflect particular human biases in a way that could be a problem?)\n", 252 | "- How will you set out to detect these specific biases before, during, and after/when communicating your analysis?\n", 253 | "- Are there any other issues related to your topic area, data, and/or analyses that are potentially problematic in terms of data privacy and equitable impact?\n", 254 | "- How will you handle issues you identified?" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "# Discusison and Conclusion\n", 262 | "\n", 263 | "Wrap it all up here. Somewhere between 3 and 10 paragraphs roughly. A good time to refer back to your Background section and review how this work extended the previous stuff. \n", 264 | "\n", 265 | "\n", 266 | "# Team Contributions\n", 267 | "\n", 268 | "Speficy who did what. This should be pretty granular, perhaps bullet points, no more than a few sentences per person." 269 | ] 270 | } 271 | ], 272 | "metadata": { 273 | "kernelspec": { 274 | "display_name": "Python 3 (ipykernel)", 275 | "language": "python", 276 | "name": "python3" 277 | }, 278 | "language_info": { 279 | "codemirror_mode": { 280 | "name": "ipython", 281 | "version": 3 282 | }, 283 | "file_extension": ".py", 284 | "mimetype": "text/x-python", 285 | "name": "python", 286 | "nbconvert_exporter": "python", 287 | "pygments_lexer": "ipython3", 288 | "version": "3.9.7" 289 | } 290 | }, 291 | "nbformat": 4, 292 | "nbformat_minor": 2 293 | } 294 | -------------------------------------------------------------------------------- /assets/CPIAUCSL.csv: -------------------------------------------------------------------------------- 1 | DATE,CPIAUCSL 2 | 1947-01-01,21.48 3 | 1947-02-01,21.62 4 | 1947-03-01,22.0 5 | 1947-04-01,22.0 6 | 1947-05-01,21.95 7 | 1947-06-01,22.08 8 | 1947-07-01,22.23 9 | 1947-08-01,22.4 10 | 1947-09-01,22.84 11 | 1947-10-01,22.91 12 | 1947-11-01,23.06 13 | 1947-12-01,23.41 14 | 1948-01-01,23.68 15 | 1948-02-01,23.67 16 | 1948-03-01,23.5 17 | 1948-04-01,23.82 18 | 1948-05-01,24.01 19 | 1948-06-01,24.15 20 | 1948-07-01,24.4 21 | 1948-08-01,24.43 22 | 1948-09-01,24.36 23 | 1948-10-01,24.31 24 | 1948-11-01,24.16 25 | 1948-12-01,24.05 26 | 1949-01-01,24.01 27 | 1949-02-01,23.91 28 | 1949-03-01,23.91 29 | 1949-04-01,23.92 30 | 1949-05-01,23.91 31 | 1949-06-01,23.92 32 | 1949-07-01,23.7 33 | 1949-08-01,23.7 34 | 1949-09-01,23.75 35 | 1949-10-01,23.67 36 | 1949-11-01,23.7 37 | 1949-12-01,23.61 38 | 1950-01-01,23.51 39 | 1950-02-01,23.61 40 | 1950-03-01,23.64 41 | 1950-04-01,23.65 42 | 1950-05-01,23.77 43 | 1950-06-01,23.88 44 | 1950-07-01,24.07 45 | 1950-08-01,24.2 46 | 1950-09-01,24.34 47 | 1950-10-01,24.5 48 | 1950-11-01,24.6 49 | 1950-12-01,24.98 50 | 1951-01-01,25.38 51 | 1951-02-01,25.83 52 | 1951-03-01,25.88 53 | 1951-04-01,25.92 54 | 1951-05-01,25.99 55 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1955-03-01,26.79 101 | 1955-04-01,26.79 102 | 1955-05-01,26.77 103 | 1955-06-01,26.71 104 | 1955-07-01,26.76 105 | 1955-08-01,26.72 106 | 1955-09-01,26.85 107 | 1955-10-01,26.82 108 | 1955-11-01,26.88 109 | 1955-12-01,26.87 110 | 1956-01-01,26.83 111 | 1956-02-01,26.86 112 | 1956-03-01,26.89 113 | 1956-04-01,26.93 114 | 1956-05-01,27.03 115 | 1956-06-01,27.15 116 | 1956-07-01,27.29 117 | 1956-08-01,27.31 118 | 1956-09-01,27.35 119 | 1956-10-01,27.51 120 | 1956-11-01,27.51 121 | 1956-12-01,27.63 122 | 1957-01-01,27.67 123 | 1957-02-01,27.8 124 | 1957-03-01,27.86 125 | 1957-04-01,27.93 126 | 1957-05-01,28.0 127 | 1957-06-01,28.11 128 | 1957-07-01,28.19 129 | 1957-08-01,28.28 130 | 1957-09-01,28.32 131 | 1957-10-01,28.32 132 | 1957-11-01,28.41 133 | 1957-12-01,28.47 134 | 1958-01-01,28.64 135 | 1958-02-01,28.7 136 | 1958-03-01,28.87 137 | 1958-04-01,28.94 138 | 1958-05-01,28.94 139 | 1958-06-01,28.91 140 | 1958-07-01,28.89 141 | 1958-08-01,28.94 142 | 1958-09-01,28.91 143 | 1958-10-01,28.91 144 | 1958-11-01,28.95 145 | 1958-12-01,28.97 146 | 1959-01-01,29.01 147 | 1959-02-01,29.0 148 | 1959-03-01,28.97 149 | 1959-04-01,28.98 150 | 1959-05-01,29.04 151 | 1959-06-01,29.11 152 | 1959-07-01,29.15 153 | 1959-08-01,29.18 154 | 1959-09-01,29.25 155 | 1959-10-01,29.35 156 | 1959-11-01,29.35 157 | 1959-12-01,29.41 158 | 1960-01-01,29.37 159 | 1960-02-01,29.41 160 | 1960-03-01,29.41 161 | 1960-04-01,29.54 162 | 1960-05-01,29.57 163 | 1960-06-01,29.61 164 | 1960-07-01,29.55 165 | 1960-08-01,29.61 166 | 1960-09-01,29.61 167 | 1960-10-01,29.75 168 | 1960-11-01,29.78 169 | 1960-12-01,29.81 170 | 1961-01-01,29.84 171 | 1961-02-01,29.84 172 | 1961-03-01,29.84 173 | 1961-04-01,29.81 174 | 1961-05-01,29.84 175 | 1961-06-01,29.84 176 | 1961-07-01,29.92 177 | 1961-08-01,29.94 178 | 1961-09-01,29.98 179 | 1961-10-01,29.98 180 | 1961-11-01,29.98 181 | 1961-12-01,30.01 182 | 1962-01-01,30.04 183 | 1962-02-01,30.11 184 | 1962-03-01,30.17 185 | 1962-04-01,30.21 186 | 1962-05-01,30.24 187 | 1962-06-01,30.21 188 | 1962-07-01,30.22 189 | 1962-08-01,30.28 190 | 1962-09-01,30.42 191 | 1962-10-01,30.38 192 | 1962-11-01,30.38 193 | 1962-12-01,30.38 194 | 1963-01-01,30.44 195 | 1963-02-01,30.48 196 | 1963-03-01,30.51 197 | 1963-04-01,30.48 198 | 1963-05-01,30.51 199 | 1963-06-01,30.61 200 | 1963-07-01,30.69 201 | 1963-08-01,30.75 202 | 1963-09-01,30.72 203 | 1963-10-01,30.75 204 | 1963-11-01,30.78 205 | 1963-12-01,30.88 206 | 1964-01-01,30.94 207 | 1964-02-01,30.91 208 | 1964-03-01,30.94 209 | 1964-04-01,30.95 210 | 1964-05-01,30.98 211 | 1964-06-01,31.01 212 | 1964-07-01,31.02 213 | 1964-08-01,31.05 214 | 1964-09-01,31.08 215 | 1964-10-01,31.12 216 | 1964-11-01,31.21 217 | 1964-12-01,31.25 218 | 1965-01-01,31.28 219 | 1965-02-01,31.28 220 | 1965-03-01,31.31 221 | 1965-04-01,31.38 222 | 1965-05-01,31.48 223 | 1965-06-01,31.61 224 | 1965-07-01,31.58 225 | 1965-08-01,31.55 226 | 1965-09-01,31.62 227 | 1965-10-01,31.65 228 | 1965-11-01,31.75 229 | 1965-12-01,31.85 230 | 1966-01-01,31.88 231 | 1966-02-01,32.08 232 | 1966-03-01,32.18 233 | 1966-04-01,32.28 234 | 1966-05-01,32.35 235 | 1966-06-01,32.38 236 | 1966-07-01,32.45 237 | 1966-08-01,32.65 238 | 1966-09-01,32.75 239 | 1966-10-01,32.85 240 | 1966-11-01,32.88 241 | 1966-12-01,32.92 242 | 1967-01-01,32.9 243 | 1967-02-01,33.0 244 | 1967-03-01,33.0 245 | 1967-04-01,33.1 246 | 1967-05-01,33.1 247 | 1967-06-01,33.3 248 | 1967-07-01,33.4 249 | 1967-08-01,33.5 250 | 1967-09-01,33.6 251 | 1967-10-01,33.7 252 | 1967-11-01,33.9 253 | 1967-12-01,34.0 254 | 1968-01-01,34.1 255 | 1968-02-01,34.2 256 | 1968-03-01,34.3 257 | 1968-04-01,34.4 258 | 1968-05-01,34.5 259 | 1968-06-01,34.7 260 | 1968-07-01,34.9 261 | 1968-08-01,35.0 262 | 1968-09-01,35.1 263 | 1968-10-01,35.3 264 | 1968-11-01,35.4 265 | 1968-12-01,35.6 266 | 1969-01-01,35.7 267 | 1969-02-01,35.8 268 | 1969-03-01,36.1 269 | 1969-04-01,36.3 270 | 1969-05-01,36.4 271 | 1969-06-01,36.6 272 | 1969-07-01,36.8 273 | 1969-08-01,36.9 274 | 1969-09-01,37.1 275 | 1969-10-01,37.3 276 | 1969-11-01,37.5 277 | 1969-12-01,37.7 278 | 1970-01-01,37.9 279 | 1970-02-01,38.1 280 | 1970-03-01,38.3 281 | 1970-04-01,38.5 282 | 1970-05-01,38.6 283 | 1970-06-01,38.8 284 | 1970-07-01,38.9 285 | 1970-08-01,39.0 286 | 1970-09-01,39.2 287 | 1970-10-01,39.4 288 | 1970-11-01,39.6 289 | 1970-12-01,39.8 290 | 1971-01-01,39.9 291 | 1971-02-01,39.9 292 | 1971-03-01,40.0 293 | 1971-04-01,40.1 294 | 1971-05-01,40.3 295 | 1971-06-01,40.5 296 | 1971-07-01,40.6 297 | 1971-08-01,40.7 298 | 1971-09-01,40.8 299 | 1971-10-01,40.9 300 | 1971-11-01,41.0 301 | 1971-12-01,41.1 302 | 1972-01-01,41.2 303 | 1972-02-01,41.4 304 | 1972-03-01,41.4 305 | 1972-04-01,41.5 306 | 1972-05-01,41.6 307 | 1972-06-01,41.7 308 | 1972-07-01,41.8 309 | 1972-08-01,41.9 310 | 1972-09-01,42.1 311 | 1972-10-01,42.2 312 | 1972-11-01,42.4 313 | 1972-12-01,42.5 314 | 1973-01-01,42.7 315 | 1973-02-01,43.0 316 | 1973-03-01,43.4 317 | 1973-04-01,43.7 318 | 1973-05-01,43.9 319 | 1973-06-01,44.2 320 | 1973-07-01,44.2 321 | 1973-08-01,45.0 322 | 1973-09-01,45.2 323 | 1973-10-01,45.6 324 | 1973-11-01,45.9 325 | 1973-12-01,46.3 326 | 1974-01-01,46.8 327 | 1974-02-01,47.3 328 | 1974-03-01,47.8 329 | 1974-04-01,48.1 330 | 1974-05-01,48.6 331 | 1974-06-01,49.0 332 | 1974-07-01,49.3 333 | 1974-08-01,49.9 334 | 1974-09-01,50.6 335 | 1974-10-01,51.0 336 | 1974-11-01,51.5 337 | 1974-12-01,51.9 338 | 1975-01-01,52.3 339 | 1975-02-01,52.6 340 | 1975-03-01,52.8 341 | 1975-04-01,53.0 342 | 1975-05-01,53.1 343 | 1975-06-01,53.5 344 | 1975-07-01,54.0 345 | 1975-08-01,54.2 346 | 1975-09-01,54.6 347 | 1975-10-01,54.9 348 | 1975-11-01,55.3 349 | 1975-12-01,55.6 350 | 1976-01-01,55.8 351 | 1976-02-01,55.9 352 | 1976-03-01,56.0 353 | 1976-04-01,56.1 354 | 1976-05-01,56.4 355 | 1976-06-01,56.7 356 | 1976-07-01,57.0 357 | 1976-08-01,57.3 358 | 1976-09-01,57.6 359 | 1976-10-01,57.9 360 | 1976-11-01,58.1 361 | 1976-12-01,58.4 362 | 1977-01-01,58.7 363 | 1977-02-01,59.3 364 | 1977-03-01,59.6 365 | 1977-04-01,60.0 366 | 1977-05-01,60.2 367 | 1977-06-01,60.5 368 | 1977-07-01,60.8 369 | 1977-08-01,61.1 370 | 1977-09-01,61.3 371 | 1977-10-01,61.6 372 | 1977-11-01,62.0 373 | 1977-12-01,62.3 374 | 1978-01-01,62.7 375 | 1978-02-01,63.0 376 | 1978-03-01,63.4 377 | 1978-04-01,63.9 378 | 1978-05-01,64.5 379 | 1978-06-01,65.0 380 | 1978-07-01,65.5 381 | 1978-08-01,65.9 382 | 1978-09-01,66.5 383 | 1978-10-01,67.1 384 | 1978-11-01,67.5 385 | 1978-12-01,67.9 386 | 1979-01-01,68.5 387 | 1979-02-01,69.2 388 | 1979-03-01,69.9 389 | 1979-04-01,70.6 390 | 1979-05-01,71.4 391 | 1979-06-01,72.2 392 | 1979-07-01,73.0 393 | 1979-08-01,73.7 394 | 1979-09-01,74.4 395 | 1979-10-01,75.2 396 | 1979-11-01,76.0 397 | 1979-12-01,76.9 398 | 1980-01-01,78.0 399 | 1980-02-01,79.0 400 | 1980-03-01,80.1 401 | 1980-04-01,80.9 402 | 1980-05-01,81.7 403 | 1980-06-01,82.5 404 | 1980-07-01,82.6 405 | 1980-08-01,83.2 406 | 1980-09-01,83.9 407 | 1980-10-01,84.7 408 | 1980-11-01,85.6 409 | 1980-12-01,86.4 410 | 1981-01-01,87.2 411 | 1981-02-01,88.0 412 | 1981-03-01,88.6 413 | 1981-04-01,89.1 414 | 1981-05-01,89.7 415 | 1981-06-01,90.5 416 | 1981-07-01,91.5 417 | 1981-08-01,92.2 418 | 1981-09-01,93.1 419 | 1981-10-01,93.4 420 | 1981-11-01,93.8 421 | 1981-12-01,94.1 422 | 1982-01-01,94.4 423 | 1982-02-01,94.7 424 | 1982-03-01,94.7 425 | 1982-04-01,95.0 426 | 1982-05-01,95.9 427 | 1982-06-01,97.0 428 | 1982-07-01,97.5 429 | 1982-08-01,97.7 430 | 1982-09-01,97.7 431 | 1982-10-01,98.1 432 | 1982-11-01,98.0 433 | 1982-12-01,97.7 434 | 1983-01-01,97.9 435 | 1983-02-01,98.0 436 | 1983-03-01,98.1 437 | 1983-04-01,98.8 438 | 1983-05-01,99.2 439 | 1983-06-01,99.4 440 | 1983-07-01,99.8 441 | 1983-08-01,100.1 442 | 1983-09-01,100.4 443 | 1983-10-01,100.8 444 | 1983-11-01,101.1 445 | 1983-12-01,101.4 446 | 1984-01-01,102.1 447 | 1984-02-01,102.6 448 | 1984-03-01,102.9 449 | 1984-04-01,103.3 450 | 1984-05-01,103.5 451 | 1984-06-01,103.7 452 | 1984-07-01,104.1 453 | 1984-08-01,104.4 454 | 1984-09-01,104.7 455 | 1984-10-01,105.1 456 | 1984-11-01,105.3 457 | 1984-12-01,105.5 458 | 1985-01-01,105.7 459 | 1985-02-01,106.3 460 | 1985-03-01,106.8 461 | 1985-04-01,107.0 462 | 1985-05-01,107.2 463 | 1985-06-01,107.5 464 | 1985-07-01,107.7 465 | 1985-08-01,107.9 466 | 1985-09-01,108.1 467 | 1985-10-01,108.5 468 | 1985-11-01,109.0 469 | 1985-12-01,109.5 470 | 1986-01-01,109.9 471 | 1986-02-01,109.7 472 | 1986-03-01,109.1 473 | 1986-04-01,108.7 474 | 1986-05-01,109.0 475 | 1986-06-01,109.4 476 | 1986-07-01,109.5 477 | 1986-08-01,109.6 478 | 1986-09-01,110.0 479 | 1986-10-01,110.2 480 | 1986-11-01,110.4 481 | 1986-12-01,110.8 482 | 1987-01-01,111.4 483 | 1987-02-01,111.8 484 | 1987-03-01,112.2 485 | 1987-04-01,112.7 486 | 1987-05-01,113.0 487 | 1987-06-01,113.5 488 | 1987-07-01,113.8 489 | 1987-08-01,114.3 490 | 1987-09-01,114.7 491 | 1987-10-01,115.0 492 | 1987-11-01,115.4 493 | 1987-12-01,115.6 494 | 1988-01-01,116.0 495 | 1988-02-01,116.2 496 | 1988-03-01,116.5 497 | 1988-04-01,117.2 498 | 1988-05-01,117.5 499 | 1988-06-01,118.0 500 | 1988-07-01,118.5 501 | 1988-08-01,119.0 502 | 1988-09-01,119.5 503 | 1988-10-01,119.9 504 | 1988-11-01,120.3 505 | 1988-12-01,120.7 506 | 1989-01-01,121.2 507 | 1989-02-01,121.6 508 | 1989-03-01,122.2 509 | 1989-04-01,123.1 510 | 1989-05-01,123.7 511 | 1989-06-01,124.1 512 | 1989-07-01,124.5 513 | 1989-08-01,124.5 514 | 1989-09-01,124.8 515 | 1989-10-01,125.4 516 | 1989-11-01,125.9 517 | 1989-12-01,126.3 518 | 1990-01-01,127.5 519 | 1990-02-01,128.0 520 | 1990-03-01,128.6 521 | 1990-04-01,128.9 522 | 1990-05-01,129.1 523 | 1990-06-01,129.9 524 | 1990-07-01,130.5 525 | 1990-08-01,131.6 526 | 1990-09-01,132.5 527 | 1990-10-01,133.4 528 | 1990-11-01,133.7 529 | 1990-12-01,134.2 530 | 1991-01-01,134.7 531 | 1991-02-01,134.8 532 | 1991-03-01,134.8 533 | 1991-04-01,135.1 534 | 1991-05-01,135.6 535 | 1991-06-01,136.0 536 | 1991-07-01,136.2 537 | 1991-08-01,136.6 538 | 1991-09-01,137.0 539 | 1991-10-01,137.2 540 | 1991-11-01,137.8 541 | 1991-12-01,138.2 542 | 1992-01-01,138.3 543 | 1992-02-01,138.6 544 | 1992-03-01,139.1 545 | 1992-04-01,139.4 546 | 1992-05-01,139.7 547 | 1992-06-01,140.1 548 | 1992-07-01,140.5 549 | 1992-08-01,140.8 550 | 1992-09-01,141.1 551 | 1992-10-01,141.7 552 | 1992-11-01,142.1 553 | 1992-12-01,142.3 554 | 1993-01-01,142.8 555 | 1993-02-01,143.1 556 | 1993-03-01,143.3 557 | 1993-04-01,143.8 558 | 1993-05-01,144.2 559 | 1993-06-01,144.3 560 | 1993-07-01,144.5 561 | 1993-08-01,144.8 562 | 1993-09-01,145.0 563 | 1993-10-01,145.6 564 | 1993-11-01,146.0 565 | 1993-12-01,146.3 566 | 1994-01-01,146.3 567 | 1994-02-01,146.7 568 | 1994-03-01,147.1 569 | 1994-04-01,147.2 570 | 1994-05-01,147.5 571 | 1994-06-01,147.9 572 | 1994-07-01,148.4 573 | 1994-08-01,149.0 574 | 1994-09-01,149.3 575 | 1994-10-01,149.4 576 | 1994-11-01,149.8 577 | 1994-12-01,150.1 578 | 1995-01-01,150.5 579 | 1995-02-01,150.9 580 | 1995-03-01,151.2 581 | 1995-04-01,151.8 582 | 1995-05-01,152.1 583 | 1995-06-01,152.4 584 | 1995-07-01,152.6 585 | 1995-08-01,152.9 586 | 1995-09-01,153.1 587 | 1995-10-01,153.5 588 | 1995-11-01,153.7 589 | 1995-12-01,153.9 590 | 1996-01-01,154.7 591 | 1996-02-01,155.0 592 | 1996-03-01,155.5 593 | 1996-04-01,156.1 594 | 1996-05-01,156.4 595 | 1996-06-01,156.7 596 | 1996-07-01,157.0 597 | 1996-08-01,157.2 598 | 1996-09-01,157.7 599 | 1996-10-01,158.2 600 | 1996-11-01,158.7 601 | 1996-12-01,159.1 602 | 1997-01-01,159.4 603 | 1997-02-01,159.7 604 | 1997-03-01,159.8 605 | 1997-04-01,159.9 606 | 1997-05-01,159.9 607 | 1997-06-01,160.2 608 | 1997-07-01,160.4 609 | 1997-08-01,160.8 610 | 1997-09-01,161.2 611 | 1997-10-01,161.5 612 | 1997-11-01,161.7 613 | 1997-12-01,161.8 614 | 1998-01-01,162.0 615 | 1998-02-01,162.0 616 | 1998-03-01,162.0 617 | 1998-04-01,162.2 618 | 1998-05-01,162.6 619 | 1998-06-01,162.8 620 | 1998-07-01,163.2 621 | 1998-08-01,163.4 622 | 1998-09-01,163.5 623 | 1998-10-01,163.9 624 | 1998-11-01,164.1 625 | 1998-12-01,164.4 626 | 1999-01-01,164.7 627 | 1999-02-01,164.7 628 | 1999-03-01,164.8 629 | 1999-04-01,165.9 630 | 1999-05-01,166.0 631 | 1999-06-01,166.0 632 | 1999-07-01,166.7 633 | 1999-08-01,167.1 634 | 1999-09-01,167.8 635 | 1999-10-01,168.1 636 | 1999-11-01,168.4 637 | 1999-12-01,168.8 638 | 2000-01-01,169.3 639 | 2000-02-01,170.0 640 | 2000-03-01,171.0 641 | 2000-04-01,170.9 642 | 2000-05-01,171.2 643 | 2000-06-01,172.2 644 | 2000-07-01,172.7 645 | 2000-08-01,172.7 646 | 2000-09-01,173.6 647 | 2000-10-01,173.9 648 | 2000-11-01,174.2 649 | 2000-12-01,174.6 650 | 2001-01-01,175.6 651 | 2001-02-01,176.0 652 | 2001-03-01,176.1 653 | 2001-04-01,176.4 654 | 2001-05-01,177.3 655 | 2001-06-01,177.7 656 | 2001-07-01,177.4 657 | 2001-08-01,177.4 658 | 2001-09-01,178.1 659 | 2001-10-01,177.6 660 | 2001-11-01,177.5 661 | 2001-12-01,177.4 662 | 2002-01-01,177.7 663 | 2002-02-01,178.0 664 | 2002-03-01,178.5 665 | 2002-04-01,179.3 666 | 2002-05-01,179.5 667 | 2002-06-01,179.6 668 | 2002-07-01,180.0 669 | 2002-08-01,180.5 670 | 2002-09-01,180.8 671 | 2002-10-01,181.2 672 | 2002-11-01,181.5 673 | 2002-12-01,181.8 674 | 2003-01-01,182.600 675 | 2003-02-01,183.600 676 | 2003-03-01,183.900 677 | 2003-04-01,183.200 678 | 2003-05-01,182.900 679 | 2003-06-01,183.100 680 | 2003-07-01,183.700 681 | 2003-08-01,184.5 682 | 2003-09-01,185.100 683 | 2003-10-01,184.9 684 | 2003-11-01,185.000 685 | 2003-12-01,185.500 686 | 2004-01-01,186.300 687 | 2004-02-01,186.700 688 | 2004-03-01,187.100 689 | 2004-04-01,187.400 690 | 2004-05-01,188.200 691 | 2004-06-01,188.900 692 | 2004-07-01,189.100 693 | 2004-08-01,189.200 694 | 2004-09-01,189.800 695 | 2004-10-01,190.8 696 | 2004-11-01,191.700 697 | 2004-12-01,191.700 698 | 2005-01-01,191.600 699 | 2005-02-01,192.400 700 | 2005-03-01,193.100 701 | 2005-04-01,193.700 702 | 2005-05-01,193.600 703 | 2005-06-01,193.700 704 | 2005-07-01,194.900 705 | 2005-08-01,196.100 706 | 2005-09-01,198.800 707 | 2005-10-01,199.100 708 | 2005-11-01,198.100 709 | 2005-12-01,198.100 710 | 2006-01-01,199.300 711 | 2006-02-01,199.400 712 | 2006-03-01,199.700 713 | 2006-04-01,200.700 714 | 2006-05-01,201.300 715 | 2006-06-01,201.800 716 | 2006-07-01,202.900 717 | 2006-08-01,203.800 718 | 2006-09-01,202.800 719 | 2006-10-01,201.900 720 | 2006-11-01,202.000 721 | 2006-12-01,203.100 722 | 2007-01-01,203.437 723 | 2007-02-01,204.226 724 | 2007-03-01,205.288 725 | 2007-04-01,205.904 726 | 2007-05-01,206.755 727 | 2007-06-01,207.234 728 | 2007-07-01,207.603 729 | 2007-08-01,207.667 730 | 2007-09-01,208.547 731 | 2007-10-01,209.190 732 | 2007-11-01,210.834 733 | 2007-12-01,211.445 734 | 2008-01-01,212.174 735 | 2008-02-01,212.687 736 | 2008-03-01,213.448 737 | 2008-04-01,213.942 738 | 2008-05-01,215.208 739 | 2008-06-01,217.463 740 | 2008-07-01,219.016 741 | 2008-08-01,218.690 742 | 2008-09-01,218.877 743 | 2008-10-01,216.995 744 | 2008-11-01,213.153 745 | 2008-12-01,211.398 746 | 2009-01-01,211.933 747 | 2009-02-01,212.705 748 | 2009-03-01,212.495 749 | 2009-04-01,212.709 750 | 2009-05-01,213.022 751 | 2009-06-01,214.790 752 | 2009-07-01,214.726 753 | 2009-08-01,215.445 754 | 2009-09-01,215.861 755 | 2009-10-01,216.509 756 | 2009-11-01,217.234 757 | 2009-12-01,217.347 758 | 2010-01-01,217.488 759 | 2010-02-01,217.281 760 | 2010-03-01,217.353 761 | 2010-04-01,217.403 762 | 2010-05-01,217.290 763 | 2010-06-01,217.199 764 | 2010-07-01,217.605 765 | 2010-08-01,217.923 766 | 2010-09-01,218.275 767 | 2010-10-01,219.035 768 | 2010-11-01,219.590 769 | 2010-12-01,220.472 770 | 2011-01-01,221.187 771 | 2011-02-01,221.898 772 | 2011-03-01,223.046 773 | 2011-04-01,224.093 774 | 2011-05-01,224.806 775 | 2011-06-01,224.806 776 | 2011-07-01,225.395 777 | 2011-08-01,226.106 778 | 2011-09-01,226.597 779 | 2011-10-01,226.750 780 | 2011-11-01,227.169 781 | 2011-12-01,227.223 782 | 2012-01-01,227.842 783 | 2012-02-01,228.329 784 | 2012-03-01,228.807 785 | 2012-04-01,229.187 786 | 2012-05-01,228.713 787 | 2012-06-01,228.524 788 | 2012-07-01,228.590 789 | 2012-08-01,229.918 790 | 2012-09-01,231.015 791 | 2012-10-01,231.638 792 | 2012-11-01,231.249 793 | 2012-12-01,231.221 794 | 2013-01-01,231.679 795 | 2013-02-01,232.937 796 | 2013-03-01,232.282 797 | 2013-04-01,231.797 798 | 2013-05-01,231.893 799 | 2013-06-01,232.445 800 | 2013-07-01,232.900 801 | 2013-08-01,233.456 802 | 2013-09-01,233.544 803 | 2013-10-01,233.669 804 | 2013-11-01,234.100 805 | 2013-12-01,234.719 806 | 2014-01-01,235.288 807 | 2014-02-01,235.547 808 | 2014-03-01,236.028 809 | 2014-04-01,236.468 810 | 2014-05-01,236.918 811 | 2014-06-01,237.231 812 | 2014-07-01,237.498 813 | 2014-08-01,237.460 814 | 2014-09-01,237.477 815 | 2014-10-01,237.430 816 | 2014-11-01,236.983 817 | 2014-12-01,236.252 818 | 2015-01-01,234.747 819 | 2015-02-01,235.342 820 | 2015-03-01,235.976 821 | 2015-04-01,236.222 822 | 2015-05-01,237.001 823 | 2015-06-01,237.657 824 | 2015-07-01,238.034 825 | 2015-08-01,238.033 826 | 2015-09-01,237.498 827 | 2015-10-01,237.733 828 | 2015-11-01,238.017 829 | 2015-12-01,237.761 830 | 2016-01-01,237.652 831 | 2016-02-01,237.336 832 | 2016-03-01,238.080 833 | 2016-04-01,238.992 834 | 2016-05-01,239.557 835 | 2016-06-01,240.222 836 | 2016-07-01,240.101 837 | 2016-08-01,240.545 838 | 2016-09-01,241.176 839 | 2016-10-01,241.741 840 | 2016-11-01,242.026 841 | 2016-12-01,242.637 842 | 2017-01-01,243.618 843 | 2017-02-01,244.006 844 | 2017-03-01,243.892 845 | 2017-04-01,244.193 846 | 2017-05-01,244.004 847 | 2017-06-01,244.163 848 | 2017-07-01,244.243 849 | 2017-08-01,245.183 850 | 2017-09-01,246.435 851 | 2017-10-01,246.626 852 | 2017-11-01,247.284 853 | 2017-12-01,247.805 854 | 2018-01-01,248.859 855 | 2018-02-01,249.529 856 | 2018-03-01,249.577 857 | 2018-04-01,250.227 858 | 2018-05-01,250.792 859 | 2018-06-01,251.018 860 | 2018-07-01,251.214 861 | 2018-08-01,251.663 862 | 2018-09-01,252.182 863 | 2018-10-01,252.772 864 | 2018-11-01,252.594 865 | 2018-12-01,252.767 866 | 2019-01-01,252.561 867 | 2019-02-01,253.319 868 | 2019-03-01,254.277 869 | 2019-04-01,255.233 870 | 2019-05-01,255.296 871 | 2019-06-01,255.213 872 | 2019-07-01,255.802 873 | 2019-08-01,256.036 874 | 2019-09-01,256.430 875 | 2019-10-01,257.155 876 | 2019-11-01,257.879 877 | 2019-12-01,258.630 878 | 2020-01-01,258.906 879 | 2020-02-01,259.246 880 | 2020-03-01,258.150 881 | 2020-04-01,256.126 882 | 2020-05-01,255.848 883 | 2020-06-01,257.004 884 | 2020-07-01,258.408 885 | 2020-08-01,259.366 886 | 2020-09-01,259.951 887 | 2020-10-01,260.249 888 | 2020-11-01,260.895 889 | 2020-12-01,262.005 890 | 2021-01-01,262.518 891 | 2021-02-01,263.583 892 | 2021-03-01,264.910 893 | 2021-04-01,266.752 894 | 2021-05-01,268.452 895 | 2021-06-01,270.664 896 | 2021-07-01,271.994 897 | 2021-08-01,272.789 898 | 2021-09-01,273.887 899 | 2021-10-01,276.434 900 | 2021-11-01,278.799 901 | 2021-12-01,280.808 902 | 2022-01-01,282.390 903 | 2022-02-01,284.535 904 | 2022-03-01,287.553 905 | 2022-04-01,288.764 906 | 2022-05-01,291.359 907 | 2022-06-01,294.996 908 | 2022-07-01,294.977 909 | 2022-08-01,295.209 910 | 2022-09-01,296.341 911 | 2022-10-01,297.863 912 | 2022-11-01,298.648 913 | 2022-12-01,298.812 914 | 2023-01-01,300.356 915 | 2023-02-01,301.509 916 | 2023-03-01,301.744 917 | 2023-04-01,303.032 918 | 2023-05-01,303.365 919 | 2023-06-01,304.003 920 | 2023-07-01,304.628 921 | 2023-08-01,306.187 922 | 2023-09-01,307.288 923 | 2023-10-01,307.531 924 | 2023-11-01,308.024 925 | 2023-12-01,308.742 926 | 2024-01-01,309.685 927 | --------------------------------------------------------------------------------