125 lines
4 KiB
Text
125 lines
4 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from datetime import datetime, timezone, timedelta\n",
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"import pytz\n",
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"\n",
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"with open(\"data/output.csv\", \"rb\") as f:\n",
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" data = pd.read_csv(f, delimiter=\"\\t\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time Played\n",
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"2024-03-04 30\n",
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"2024-05-01 26\n",
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"2024-04-13 26\n",
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"2024-04-25 25\n",
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"2024-04-14 24\n",
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"2024-04-20 23\n",
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"2024-04-18 20\n",
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"2024-04-24 19\n",
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"2024-03-05 17\n",
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"2024-03-10 17\n",
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"Name: Time Played, dtype: int64\n"
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]
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}
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],
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"source": [
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"JST = pytz.timezone(\"Asia/Tokyo\")\n",
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"CST = pytz.timezone(\"America/Chicago\")\n",
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"\n",
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"def to_jst_timestamp(s: str, format_str=\"%Y-%m-%d %H:%M:%S\"):\n",
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" if type(s) is not str: return None\n",
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" naive_dt = datetime.strptime(s, format_str)\n",
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" jst_dt = JST.localize(naive_dt)\n",
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" cst_dt = jst_dt.astimezone(CST)\n",
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" return cst_dt\n",
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"\n",
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"times = data[\"Time Played\"]\n",
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"times_mapped = times.map(to_jst_timestamp)\n",
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"days_played = times_mapped.groupby(times_mapped.dt.date).count()\n",
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"print(days_played.sort_values(ascending=False).head(10))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Clears Average Song Name Score Lamp\n",
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"Rating \n",
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"8 7 895073 朧 (dj TAKA Remix) 996620 GFC\n",
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"9 25 918551 Why not 999650 PFC\n",
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"10 29 921425 隅田川夏恋歌 992160 GFC\n",
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"11 40 896780 朧 992520 GFC\n",
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"12 56 890122 MY SUMMER LOVE 987030 GFC\n",
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"13 70 840879 Struggle 978430 GFC\n",
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"14 121 799834 FUNKY SUMMER BEACH 943140 FC\n",
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"15 26 798904 未来（ダ）FUTURE 898400 Clear\n",
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"16 16 770314 Sword of Vengeance 861210 Clear\n",
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"17 1 662870 PRANA+REVOLUTIONARY ADDICT (U1 DJ Mix) 662870 Clear\n"
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]
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}
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],
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"source": [
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"records_by_level = data.loc[data[\"Lamp\"] != \"Fail\"].groupby(data[\"Rating\"])\n",
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"num_clears_by_level = records_by_level[\"Score\"].count().rename(\"Clears\")\n",
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"average_scores_by_level = records_by_level[\"Score\"].mean().round().astype(\"int\").rename(\"Average\")\n",
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"def max_info(group):\n",
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" max_idx = group[\"Score\"].idxmax()\n",
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" return group.loc[max_idx]\n",
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"max_score_by_level = records_by_level.apply(max_info)[[\"Score\", \"Lamp\", \"Song Name\"]]\n",
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"result = pd.merge(num_clears_by_level, average_scores_by_level, on=\"Rating\")\n",
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"result = pd.merge(result, max_score_by_level, on=\"Rating\")\n",
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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