diff --git a/experiments/votes_by_year/adjust.py b/experiments/votes_by_year/adjust.py new file mode 100644 index 0000000000000000000000000000000000000000..89378559295d4f6c07199fb13d8c66c6aed9523e --- /dev/null +++ b/experiments/votes_by_year/adjust.py @@ -0,0 +1,155 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import json +from datetime import date +from pathlib import Path + +import jupyter_black +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +from pytility import parse_date +from tqdm import tqdm + +jupyter_black.load() + +SEED = 23 + + +# %% +def process_row( + item, + columns=( + "item_id", + "bgg_id", + "bgg_user_name", + "bgg_user_owned", + "bgg_user_play_count", + "bgg_user_rating", + "updated_at", + ), +): + item = json.loads(item) + if isinstance(item, dict) and all( + item.get(column) is not None for column in columns + ): + return {column: item[column] for column in columns} + return None + + +def df_from_jl(path): + with open(path) as f: + records = filter(None, map(process_row, tqdm(f))) + df = pd.DataFrame.from_records(records, index="item_id") + df["year"] = df.updated_at.apply(lambda x: x[:4]).astype("int32") + return df + + +# %% +path_csv = Path("bgg_RatingItem.csv").resolve() +path_jl = Path("../../../board-game-data/scraped/bgg_RatingItem.jl").resolve() +path_games = Path("../../../board-game-data/scraped/bgg_GameItem.csv").resolve() + +# %% +try: + df = pd.read_csv(path_csv, index_col="item_id", dtype={"item_id": "object"}) +except Exception: + df = df_from_jl(path_jl) +df.shape + +# %% +df.sample(5, random_state=SEED).T + +# %% +if not path_csv.exists(): + df.to_csv(path_csv) + +# %% +by_year = df.groupby("year").bgg_id.count() +by_year + +# %% +now = date.today() +eoy = date(now.year, 12, 31) + +# %% +eoy_adj = by_year[now.year] * eoy.timetuple().tm_yday / now.timetuple().tm_yday +eoy_adj + +# %% +by_year[by_year.index < now.year].plot(style="k-", linewidth=3) +plt.plot([now.year - 1, now.year], [by_year[now.year - 1], eoy_adj], "k:", linewidth=3) +plt.tight_layout() +plt.savefig("ratings_by_year.svg") +plt.show() + +# %% +games = pd.read_csv(path_games, index_col="bgg_id") +games.compilation = games.compilation.notnull() +games.cooperative = games.cooperative.notnull() +games.shape + +# %% +games.sample(5, random_state=SEED).T + +# %% +votes_by_year = games.groupby("year").num_votes.agg(["sum", "mean", "median"]) + +# %% +votes_by_year[(votes_by_year.index >= 1980) & (votes_by_year.index <= 2020)][ + "sum" +].plot(style="k-", linewidth=3) + +# %% +votes_by_year[(votes_by_year.index >= 1980) & (votes_by_year.index <= 2020)][ + "mean" +].plot(style="k-", linewidth=3) + +# %% +votes_by_year[(votes_by_year.index >= 1980) & (votes_by_year.index <= 2020)][ + "median" +].plot(style="k-", linewidth=3) + +# %% +votes_by_year_ranked = ( + games[~games.compilation & games["rank"].notnull() & games.bayes_rating.notnull()] + .groupby("year") + .num_votes.agg(["sum", "mean", "median"]) +) + +# %% +votes_by_year_ranked[ + (votes_by_year_ranked.index >= 1980) & (votes_by_year_ranked.index <= 2020) +]["sum"].plot(style="k-", linewidth=3) + +# %% +votes_by_year_ranked[ + (votes_by_year_ranked.index >= 1980) & (votes_by_year_ranked.index <= 2020) +]["mean"].plot(style="k-", linewidth=3) + +# %% +votes_by_year_ranked[ + (votes_by_year_ranked.index >= 1980) & (votes_by_year_ranked.index <= 2020) +]["median"].plot(style="k-", linewidth=3) + +# %% +sns.violinplot( + x="year", + y="num_votes", + data=games[(games.year >= 1990) & (games.year <= 2000)], +) +plt.ylim(0, 1_000) diff --git a/experiments/votes_by_year/poetry.lock b/experiments/votes_by_year/poetry.lock new file mode 100644 index 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