From e9cc3c297a2945e4b3490fe4d382743e595423b3 Mon Sep 17 00:00:00 2001 From: 100gle <569590461@qq.com> Date: Tue, 19 Apr 2022 20:57:50 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=9B=B4=E6=96=B0=E5=BC=95=E5=85=A5?= =?UTF-8?q?=E6=A1=88=E4=BE=8B=E7=9B=B8=E5=85=B3=E6=BA=90=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/02/bilibili.py | 171 +++++ code/02/fetchVideoStat.js | 25 + code/02/main.ipynb | 1414 +++++++++++++++++++++++++++++++++++++ data/bilibili.csv | 282 ++++++++ 4 files changed, 1892 insertions(+) create mode 100644 code/02/bilibili.py create mode 100644 code/02/fetchVideoStat.js create mode 100644 code/02/main.ipynb create mode 100644 data/bilibili.csv diff --git a/code/02/bilibili.py b/code/02/bilibili.py new file mode 100644 index 0000000..b10e9be --- /dev/null +++ b/code/02/bilibili.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 +# coding:utf-8 + +import itertools +import logging +import math +import pathlib +from concurrent.futures import ThreadPoolExecutor +from typing import Dict, List, TypeVar + +import pandas as pd +import requests +import requests_html + +# -------------------- +# Prerequisites +# -------------------- + +# internal types +_T = TypeVar("_T") +APIData = Dict[str, _T] +Records = List[APIData] + +# api or data url +VIDEO_API_URL = "https://api.bilibili.com/x/space/arc/search" +VIDEO_PAGE_URL = "https://www.bilibili.com/video/{bvid}" + +# request header +USER_AGENTS = [ + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_2)", + "AppleWebKit/537.36 (KHTML, like Gecko)", + "Chrome/81.0.4044.92", + "Safari/537.36", + "Edg/81.0.416.53", +] +HEADERS = {"User-Agent": " ".join(USER_AGENTS)} + +MID_NUMBER = "533459953" +params = {"ps": "30", "tid": "0", "order": "pubdate"} + + +# logger +logging.basicConfig( + level=logging.INFO, + format="[{asctime}]-[{levelname:<8}]-[{funcName}:{lineno}] - {message}", + datefmt="%Y-%m-%d %H:%M:%S", + style="{", +) +log = logging.getLogger(__name__) + + +# ----------------------- +# Query and Handle Method +# ----------------------- + + +def fetch_page_number(mid: str) -> int: + """fetch total page number from API at first time query.""" + + total = 0 + payloads = {"mid": mid, "pn": 1, **params} + with requests.Session() as sess: + response = sess.get( + url=VIDEO_API_URL, + headers=HEADERS, + params=payloads, + ) + response.raise_for_status() + + count = response.json()["data"]["page"]["count"] + total += math.ceil(int(count) / 30) + + return total + + +def fetch_video_data(mid: str, page: int) -> List[APIData]: + """fetch video data from API.""" + + payload = {"mid": mid, "pn": str(page), **params} + with requests.Session() as sess: + response = sess.get( + url=VIDEO_API_URL, + headers=HEADERS, + params=payload, + ) + + response.raise_for_status() + + jsons = response.json()["data"]["list"]["vlist"] + log.info(f"fetch video from '{mid}' at {page} page.") + return jsons + + +async def fetch_stats(bvid: str, asess) -> APIData: + """fetch like, coin, collect and share from video page.""" + + info = {} + stats = ["rank", "like", "coin", "collect", "share"] + response = await asess.get( + url=VIDEO_PAGE_URL.format(bvid=bvid), + headers=HEADERS, + ) + response.raise_for_status() + html = response.html + + has_rank = html.find(".video-data .rank", first=True) + if has_rank: + info["rank"] = has_rank.text.strip() + + try: + info["like"] = html.find(".ops .like", first=True).text.strip() + info["coin"] = html.find(".ops .coin", first=True).text.strip() + info["collect"] = html.find(".ops .collect", first=True).text.strip() + info["share"] = html.find(".ops .share", first=True).text.strip() + except AttributeError: + log.warning(f"cant' get stats from '{bvid}', use default.") + return {k: "" for k in stats} + + log.info(f"fetch stats from '{bvid}'.") + return info + + +async def bundle(json, asess) -> APIData: + """bundle json data with stats.""" + + bvid = json["bvid"] + stats = await fetch_stats(bvid, asess) + info = {**json, **stats} + return info + + +def query(mid: str) -> Records: + """query data by mid number.""" + + log.info(f"querying data from '{mid}'...") + + total_page = fetch_page_number(mid) + with ThreadPoolExecutor(max_workers=2) as p: + features = [ + p.submit(fetch_video_data, mid=mid, page=page) + for page in range(1, total_page + 1) + ] + jsons = itertools.chain(*[f.result() for f in features]) + + # async session for html request + asess = requests_html.AsyncHTMLSession(workers=2) + + # compatible with requests-html async coroutine codes + # see: https://github.com/psf/requests-html/issues/362 + results = asess.run( + *[lambda json=json, asess=asess: bundle(json, asess) for json in jsons] + ) + return results + + +def parse(jsons: Records) -> pd.DataFrame: + """normalize and combine json data.""" + + return pd.json_normalize(jsons) + + +def main(): + csvfile = pathlib.Path("~/Desktop/bilibili.csv").expanduser() + + jsons = query(MID_NUMBER) + data = parse(jsons) + data.to_csv(csvfile, index=False) + + +if __name__ == "__main__": + main() diff --git a/code/02/fetchVideoStat.js b/code/02/fetchVideoStat.js new file mode 100644 index 0000000..bc626e1 --- /dev/null +++ b/code/02/fetchVideoStat.js @@ -0,0 +1,25 @@ +(function () { + let api = window.__INITIAL_STATE__; + let stat = undefined; + try { + stat = api.videoData.stat; + } catch (e) {} + + const cols = ["rank", "like", "coin", "collect", "share"]; + + let blankStat = {}; + for (const col of cols) { + blankStat[col] = ""; + } + + if (stat === undefined) { + return blankStat; + } + return { + rank: stat.his_rank, + like: stat.like, + coin: stat.coin, + collect: stat.favorite, + share: stat.share, + }; +})(); diff --git a/code/02/main.ipynb b/code/02/main.ipynb new file mode 100644 index 0000000..e64952a --- /dev/null +++ b/code/02/main.ipynb @@ -0,0 +1,1414 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_formats=[\"svg\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme()\n", + "style_settings = {\n", + " \"font.sans-serif\": [\"Songti SC\", \"SimHei\"],\n", + " \"axes.unicode_minus\": False,\n", + "}\n", + "sns.set_style(\"darkgrid\", style_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# # use the desktop data\n", + "# csvfile = pathlib.Path(\"~/Desktop/bilibili.csv\").expanduser()\n", + "\n", + "# use default data\n", + "csvfile = pathlib.Path(\"../../data/bilibili.csv\")\n", + "\n", + "raw = pd.read_csv(csvfile)" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 281 entries, 0 to 280\n", + "Data columns (total 26 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 comment 281 non-null int64 \n", + " 1 typeid 281 non-null int64 \n", + " 2 play 281 non-null int64 \n", + " 3 pic 281 non-null object \n", + " 4 subtitle 0 non-null float64\n", + " 5 description 279 non-null object \n", + " 6 copyright 281 non-null int64 \n", + " 7 title 281 non-null object \n", + " 8 review 281 non-null int64 \n", + " 9 author 281 non-null object \n", + " 10 mid 281 non-null int64 \n", + " 11 created 281 non-null int64 \n", + " 12 length 281 non-null object \n", + " 13 video_review 281 non-null int64 \n", + " 14 aid 281 non-null int64 \n", + " 15 bvid 281 non-null object \n", + " 16 hide_click 281 non-null bool \n", + " 17 is_pay 281 non-null int64 \n", + " 18 is_union_video 281 non-null int64 \n", + " 19 is_steins_gate 281 non-null int64 \n", + " 20 is_live_playback 281 non-null int64 \n", + " 21 like 279 non-null object \n", + " 22 coin 279 non-null object \n", + " 23 collect 279 non-null object \n", + " 24 share 279 non-null object \n", + " 25 rank 102 non-null object \n", + "dtypes: bool(1), float64(1), int64(13), object(11)\n", + "memory usage: 55.3+ KB\n" + ] + } + ], + "source": [ + "raw.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " comment typeid play subtitle copyright review \\\ncount 281.000000 281.000000 2.810000e+02 0.0 281.0 281.0 \nmean 3587.989324 168.017794 2.768051e+06 NaN 1.0 0.0 \nstd 4407.248268 78.913400 1.754950e+06 NaN 0.0 0.0 \nmin 172.000000 21.000000 3.336690e+05 NaN 1.0 0.0 \n25% 1286.000000 212.000000 1.648079e+06 NaN 1.0 0.0 \n50% 2577.000000 212.000000 2.435455e+06 NaN 1.0 0.0 \n75% 4639.000000 212.000000 3.415756e+06 NaN 1.0 0.0 \nmax 46792.000000 239.000000 1.565324e+07 NaN 1.0 0.0 \n\n mid created video_review aid is_pay \\\ncount 281.0 2.810000e+02 281.000000 2.810000e+02 281.0 \nmean 533459953.0 1.632178e+09 6372.619217 5.872551e+08 0.0 \nstd 0.0 8.106261e+06 8871.608323 2.255457e+08 0.0 \nmin 533459953.0 1.617385e+09 51.000000 2.049769e+08 0.0 \n25% 533459953.0 1.625652e+09 1430.000000 3.806299e+08 0.0 \n50% 533459953.0 1.631695e+09 3514.000000 5.904713e+08 0.0 \n75% 533459953.0 1.638846e+09 7998.000000 7.637649e+08 0.0 \nmax 533459953.0 1.648907e+09 82150.000000 9.778694e+08 0.0 \n\n is_union_video is_steins_gate is_live_playback \ncount 281.0 281.0 281.0 \nmean 0.0 0.0 0.0 \nstd 0.0 0.0 0.0 \nmin 0.0 0.0 0.0 \n25% 0.0 0.0 0.0 \n50% 0.0 0.0 0.0 \n75% 0.0 0.0 0.0 \nmax 0.0 0.0 0.0 ", + "text/html": "
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" + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.describe(include=\"object\")" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "mid\n533459953 281\ndtype: int64" + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.value_counts(\"mid\")" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# target columns\n", + "cols = [\n", + " # BVID 号\n", + " \"bvid\",\n", + " # UP 主频道号\n", + " \"mid\",\n", + " # UP 主名称\n", + " \"author\",\n", + " # 视频标题\n", + " \"title\",\n", + " # 视频上传时间\n", + " \"created\",\n", + " # 视频长度\n", + " \"length\",\n", + " # 视频播放量\n", + " \"play\",\n", + " # 视频评论数\n", + " \"comment\",\n", + " # 视频弹幕数\n", + " \"video_review\",\n", + " # 视频全站排名（第 N 名或无）\n", + " \"rank\",\n", + " # 视频点赞量\n", + " \"like\",\n", + " # 视频投币数\n", + " \"coin\",\n", + " # 视频收藏量\n", + " \"collect\",\n", + " # 视频分享量\n", + " \"share\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " mid author title created length \\\nbvid \nBV19r4y1r7wx 533459953 刘庸干净又卫生 印度阿姆利则降旗仪式，富有娱乐精神的表演。 1636948887 00:20 \nBV14r4y1y7Ln 533459953 刘庸干净又卫生 发烧怎么办？看开挂的民族如何应对！ 1634526240 02:24 \nBV1Rq4y1Z7TT 533459953 刘庸干净又卫生 依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。 1631094362 00:50 \nBV1n54y1E7D5 533459953 刘庸干净又卫生 印度新德里街头水果摊大全，堪比超市！ 1626083657 00:59 \nBV1Yf4y1t7VV 533459953 刘庸干净又卫生 这什么玩意儿？买了个寂寞！ 1624540361 00:55 \n\n play comment video_review rank like coin collect share \nbvid \nBV19r4y1r7wx 1695848 2073 1050 NaN 7.9万 2182 2088 1856 \nBV14r4y1y7Ln 2240231 6026 6124 NaN 9.5万 5265 3241 8044 \nBV1Rq4y1Z7TT 2435455 2875 1766 NaN 10.4万 8106 2410 1844 \nBV1n54y1E7D5 959967 807 1070 NaN 3.2万 976 658 472 \nBV1Yf4y1t7VV 2163298 596 853 NaN 5.1万 1483 1289 481 ", + "text/html": "
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" + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw: pd.DataFrame = raw.astype({\"mid\": \"str\"}).filter(items=cols).set_index(\"bvid\")\n", + "raw.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据清洗与处理" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " mid author title \\\nbvid \nBV19r4y1r7wx 533459953 刘庸干净又卫生 印度阿姆利则降旗仪式，富有娱乐精神的表演。 \nBV14r4y1y7Ln 533459953 刘庸干净又卫生 发烧怎么办？看开挂的民族如何应对！ \nBV1Rq4y1Z7TT 533459953 刘庸干净又卫生 依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。 \nBV1n54y1E7D5 533459953 刘庸干净又卫生 印度新德里街头水果摊大全，堪比超市！ \nBV1Yf4y1t7VV 533459953 刘庸干净又卫生 这什么玩意儿？买了个寂寞！ \n\n created play comment video_review rank \\\nbvid \nBV19r4y1r7wx 2021-11-15 04:01:27 1695848 2073 1050 0 \nBV14r4y1y7Ln 2021-10-18 03:04:00 2240231 6026 6124 0 \nBV1Rq4y1Z7TT 2021-09-08 09:46:02 2435455 2875 1766 0 \nBV1n54y1E7D5 2021-07-12 09:54:17 959967 807 1070 0 \nBV1Yf4y1t7VV 2021-06-24 13:12:41 2163298 596 853 0 \n\n created_type \nbvid \nBV19r4y1r7wx 00h-07h \nBV14r4y1y7Ln 00h-07h \nBV1Rq4y1Z7TT 08h-16h \nBV1n54y1E7D5 08h-16h \nBV1Yf4y1t7VV 08h-16h ", + "text/html": "
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BV19r4y1r7wx533459953刘庸干净又卫生印度阿姆利则降旗仪式，富有娱乐精神的表演。2021-11-15 04:01:27169584820731050000h-07h
BV14r4y1y7Ln533459953刘庸干净又卫生发烧怎么办？看开挂的民族如何应对！2021-10-18 03:04:00224023160266124000h-07h
BV1Rq4y1Z7TT533459953刘庸干净又卫生依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。2021-09-08 09:46:02243545528751766008h-16h
BV1n54y1E7D5533459953刘庸干净又卫生印度新德里街头水果摊大全，堪比超市！2021-07-12 09:54:179599678071070008h-16h
BV1Yf4y1t7VV533459953刘庸干净又卫生这什么玩意儿？买了个寂寞！2021-06-24 13:12:412163298596853008h-16h
\n
" + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tidy common\n", + "\n", + "\n", + "def divide_hour(hour: int):\n", + " if 8 <= hour < 17:\n", + " return \"08h-16h\"\n", + " elif 17 <= hour < 24:\n", + " return \"17h-23h\"\n", + " else:\n", + " return \"00h-07h\"\n", + "\n", + "\n", + "ops = [\"like\", \"coin\", \"collect\", \"share\"]\n", + "\n", + "common: pd.DataFrame = raw.assign(\n", + " title=raw[\"title\"].str.strip(),\n", + " created=raw[\"created\"].apply(pd.to_datetime, unit=\"s\"),\n", + " created_type=lambda data: (\n", + " data[\"created\"].dt.hour.map(divide_hour).pipe(\n", + " pd.Categorical,\n", + " ordered=True,\n", + " categories=[\"08h-16h\", \"17h-23h\", \"00h-07h\"],\n", + " )\n", + " ),\n", + " rank=(\n", + " raw[\"rank\"]\n", + " .fillna(\"\")\n", + " .str.extract(r\"(?P\\d+)\")\n", + " .fillna(0)\n", + " .astype(\"int\")\n", + " ),\n", + ").drop([*ops, \"length\"], axis=1)\n", + "\n", + "common.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "outputs": [ + { + "data": { + "text/plain": " mid author title \\\nbvid \nBV19r4y1r7wx 533459953 刘庸干净又卫生 印度阿姆利则降旗仪式，富有娱乐精神的表演。 \nBV14r4y1y7Ln 533459953 刘庸干净又卫生 发烧怎么办？看开挂的民族如何应对！ \nBV1Rq4y1Z7TT 533459953 刘庸干净又卫生 依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。 \nBV1n54y1E7D5 533459953 刘庸干净又卫生 印度新德里街头水果摊大全，堪比超市！ \nBV1Yf4y1t7VV 533459953 刘庸干净又卫生 这什么玩意儿？买了个寂寞！ \n\n created play comment video_review rank \\\nbvid \nBV19r4y1r7wx 2021-11-15 04:01:27 1695848 2073 1050 0 \nBV14r4y1y7Ln 2021-10-18 03:04:00 2240231 6026 6124 0 \nBV1Rq4y1Z7TT 2021-09-08 09:46:02 2435455 2875 1766 0 \nBV1n54y1E7D5 2021-07-12 09:54:17 959967 807 1070 0 \nBV1Yf4y1t7VV 2021-06-24 13:12:41 2163298 596 853 0 \n\n created_type \nbvid \nBV19r4y1r7wx 00h-07h \nBV14r4y1y7Ln 00h-07h \nBV1Rq4y1Z7TT 08h-16h \nBV1n54y1E7D5 08h-16h \nBV1Yf4y1t7VV 08h-16h ", + "text/html": "
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midauthortitlecreatedplaycommentvideo_reviewrankcreated_type
bvid
BV19r4y1r7wx533459953刘庸干净又卫生印度阿姆利则降旗仪式，富有娱乐精神的表演。2021-11-15 04:01:27169584820731050000h-07h
BV14r4y1y7Ln533459953刘庸干净又卫生发烧怎么办？看开挂的民族如何应对！2021-10-18 03:04:00224023160266124000h-07h
BV1Rq4y1Z7TT533459953刘庸干净又卫生依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。2021-09-08 09:46:02243545528751766008h-16h
BV1n54y1E7D5533459953刘庸干净又卫生印度新德里街头水果摊大全，堪比超市！2021-07-12 09:54:179599678071070008h-16h
BV1Yf4y1t7VV533459953刘庸干净又卫生这什么玩意儿？买了个寂寞！2021-06-24 13:12:412163298596853008h-16h
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" + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "common.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " like coin collect share\nbvid \nBV19r4y1r7wx 79000 2182 2088 1856\nBV14r4y1y7Ln 95000 5265 3241 8044\nBV1Rq4y1Z7TT 104000 8106 2410 1844\nBV1n54y1E7D5 32000 976 658 472\nBV1Yf4y1t7VV 51000 1483 1289 481", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
likecoincollectshare
bvid
BV19r4y1r7wx79000218220881856
BV14r4y1y7Ln95000526532418044
BV1Rq4y1Z7TT104000810624101844
BV1n54y1E7D532000976658472
BV1Yf4y1t7VV5100014831289481
\n
" + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make operators data numeric.\n", + "\n", + "def numeric_ops(text: str):\n", + " if \"万\" in text:\n", + " digits = float(text.replace(\"万\", \"\")) * 10000\n", + " return int(digits)\n", + " else:\n", + " return int(text)\n", + "\n", + "operators: pd.DataFrame = (\n", + " raw.filter(items=ops)\n", + " .fillna(\"0\")\n", + " .applymap(numeric_ops)\n", + " .astype(\"int\")\n", + ")\n", + "\n", + "operators.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " seconds\nbvid \nBV19r4y1r7wx 20\nBV14r4y1y7Ln 144\nBV1Rq4y1Z7TT 50\nBV1n54y1E7D5 59\nBV1Yf4y1t7VV 55", + "text/html": "
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seconds
bvid
BV19r4y1r7wx20
BV14r4y1y7Ln144
BV1Rq4y1Z7TT50
BV1n54y1E7D559
BV1Yf4y1t7VV55
\n
" + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# length to seconds\n", + "\n", + "timer: pd.DataFrame = (\n", + " raw[\"length\"].str\n", + " .extract(r\"(?P\\d{2}):(?P\\d{2})\")\n", + " .astype(\"int\")\n", + " .assign(seconds=lambda data: data[\"minute\"] * 60 + data[\"second\"])\n", + " .filter(like=\"seconds\")\n", + ")\n", + "\n", + "\n", + "timer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n0 BV1Rh411D755 533459953 刘庸干净又卫生 原创首发 \n1 BV175411A7L3 533459953 刘庸干净又卫生 喝一个养颜美容芦荟汁 \n2 BV1jK4y1m7Dc 533459953 刘庸干净又卫生 刷刷鞋 \n3 BV1M54y1b7tD 533459953 刘庸干净又卫生 我知道你们早就看过了，我只是怀念一下而已。 \n4 BV1nb4y1D77D 533459953 刘庸干净又卫生 天热买个冰棍儿 \n\n created play comment video_review rank created_type \\\n0 2021-04-02 17:31:29 1769938 2570 4809 0 17h-23h \n1 2021-04-05 12:46:03 1865198 1802 5065 0 08h-16h \n2 2021-04-06 17:00:02 761236 311 535 0 17h-23h \n3 2021-04-07 15:35:02 2190407 919 1017 0 08h-16h \n4 2021-04-08 16:49:32 1682582 453 956 0 08h-16h \n\n like coin collect share seconds since_last_days \n0 65000 6296 7349 3728 277 0 \n1 70000 16000 9947 17000 131 3 \n2 16000 509 503 120 85 2 \n3 53000 5952 6343 1476 59 1 \n4 42000 1322 1393 519 54 2 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
0BV1Rh411D755533459953刘庸干净又卫生原创首发2021-04-02 17:31:29176993825704809017h-23h650006296734937282770
1BV175411A7L3533459953刘庸干净又卫生喝一个养颜美容芦荟汁2021-04-05 12:46:03186519818025065008h-16h70000160009947170001313
2BV1jK4y1m7Dc533459953刘庸干净又卫生刷刷鞋2021-04-06 17:00:02761236311535017h-23h16000509503120852
3BV1M54y1b7tD533459953刘庸干净又卫生我知道你们早就看过了，我只是怀念一下而已。2021-04-07 15:35:0221904079191017008h-16h53000595263431476591
4BV1nb4y1D77D533459953刘庸干净又卫生天热买个冰棍儿2021-04-08 16:49:321682582453956008h-16h4200013221393519542
\n
" + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# combine common, operators and timer frame together.\n", + "\n", + "df: pd.DataFrame = (\n", + " pd.concat(\n", + " [common, operators, timer],\n", + " axis=1,\n", + " join=\"inner\",\n", + " verify_integrity=True,\n", + " )\n", + " .sort_values(by=\"created\")\n", + " .assign(\n", + " since_last_days=lambda data: (\n", + " data[\"created\"]\n", + " .diff()\n", + " .dt.ceil(\"d\")\n", + " .dt.days\n", + " .fillna(0)\n", + " .astype(\"int\")\n", + " )\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "bvid object\nmid object\nauthor object\ntitle object\ncreated datetime64[ns]\nplay int64\ncomment int64\nvideo_review int64\nrank int64\ncreated_type category\nlike int64\ncoin int64\ncollect int64\nshare int64\nseconds int64\nsince_last_days int64\ndtype: object" + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 数据分析与可视化" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " created play comment \\\ncount 281 2.810000e+02 281.000000 \nmean 2021-09-20 22:42:29.647686656 2.768051e+06 3587.989324 \nmin 2021-04-02 17:31:29 3.336690e+05 172.000000 \n50% 2021-09-15 08:43:32 2.435455e+06 2577.000000 \nmax 2022-04-02 13:40:04 1.565324e+07 46792.000000 \nstd NaN 1.754950e+06 4407.248268 \n\n video_review rank like coin collect \\\ncount 281.000000 281.000000 2.810000e+02 281.000000 281.000000 \nmean 6372.619217 14.103203 1.346215e+05 13328.181495 5659.747331 \nmin 51.000000 0.000000 0.000000e+00 0.000000 0.000000 \n50% 3514.000000 0.000000 9.500000e+04 5839.000000 3398.000000 \nmax 82150.000000 91.000000 1.124000e+06 350000.000000 89000.000000 \nstd 8871.608323 23.908158 1.282603e+05 30758.026709 8698.436615 \n\n share seconds since_last_days \ncount 281.000000 281.000000 281.000000 \nmean 6202.003559 155.227758 1.782918 \nmin 0.000000 9.000000 0.000000 \n50% 2751.000000 76.000000 2.000000 \nmax 106000.000000 2877.000000 7.000000 \nstd 12144.340569 327.588938 0.909784 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
createdplaycommentvideo_reviewranklikecoincollectsharesecondssince_last_days
count2812.810000e+02281.000000281.000000281.0000002.810000e+02281.000000281.000000281.000000281.000000281.000000
mean2021-09-20 22:42:29.6476866562.768051e+063587.9893246372.61921714.1032031.346215e+0513328.1814955659.7473316202.003559155.2277581.782918
min2021-04-02 17:31:293.336690e+05172.00000051.0000000.0000000.000000e+000.0000000.0000000.0000009.0000000.000000
50%2021-09-15 08:43:322.435455e+062577.0000003514.0000000.0000009.500000e+045839.0000003398.0000002751.00000076.0000002.000000
max2022-04-02 13:40:041.565324e+0746792.00000082150.00000091.0000001.124000e+06350000.00000089000.000000106000.0000002877.0000007.000000
stdNaN1.754950e+064407.2482688871.60832323.9081581.282603e+0530758.0267098698.43661512144.340569327.5889380.909784
\n
" + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe(percentiles=[0.5], datetime_is_numeric=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n184 BV16S4y1R7Vd 533459953 刘庸干净又卫生 出门吃个简易早餐，经济又实惠，干净又卫生！ \n214 BV1QQ4y1e7dB 533459953 刘庸干净又卫生 印度街头吃点烧烤！ \n\n created play comment video_review rank created_type \\\n184 2021-11-02 03:30:18 3852133 4766 9329 0 00h-07h \n214 2021-12-11 03:00:10 3867097 6189 29091 0 00h-07h \n\n like coin collect share seconds since_last_days \n184 0 0 0 0 209 1 \n214 0 0 0 0 203 2 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
184BV16S4y1R7Vd533459953刘庸干净又卫生出门吃个简易早餐，经济又实惠，干净又卫生！2021-11-02 03:30:18385213347669329000h-07h00002091
214BV1QQ4y1e7dB533459953刘庸干净又卫生印度街头吃点烧烤！2021-12-11 03:00:103867097618929091000h-07h00002032
\n
" + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"like\"]==0]" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### 视频排名情况" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n251 BV1AY41187b3 533459953 刘庸干净又卫生 久等了，昏昏沉沉好几天，今天清醒多了。 \n252 BV16F411H7gW 533459953 刘庸干净又卫生 历时8天，我康复了，谢谢所有的朋友们。 \n248 BV1Lr4y1e7WH 533459953 刘庸干净又卫生 印度白领区的街头咖啡，非常的好喝。 \n197 BV18r4y1Q7sE 533459953 刘庸干净又卫生 应广发粉丝的要求，印度晚上街头走一走，闲聊几句！ \n249 BV1qS4y1o7Yk 533459953 刘庸干净又卫生 快过年了，提前吃点好吃的。 \n\n created play comment video_review rank created_type \\\n251 2022-01-25 05:00:05 8986409 35247 82150 1 00h-07h \n252 2022-01-30 04:58:33 7348868 30466 66680 1 00h-07h \n248 2022-01-18 10:05:09 4056024 5467 10100 2 08h-16h \n197 2021-11-24 03:00:04 4162761 14103 38706 3 00h-07h \n249 2022-01-19 11:00:18 6794331 14981 25761 3 08h-16h \n\n like coin collect share seconds since_last_days \n251 1124000 350000 65000 48000 257 5 \n252 1011000 289000 60000 62000 105 5 \n248 262000 9782 6916 4307 91 1 \n197 285000 108000 27000 18000 908 1 \n249 481000 49000 20000 16000 164 2 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
251BV1AY41187b3533459953刘庸干净又卫生久等了，昏昏沉沉好几天，今天清醒多了。2022-01-25 05:00:0589864093524782150100h-07h112400035000065000480002575
252BV16F411H7gW533459953刘庸干净又卫生历时8天，我康复了，谢谢所有的朋友们。2022-01-30 04:58:3373488683046666680100h-07h101100028900060000620001055
248BV1Lr4y1e7WH533459953刘庸干净又卫生印度白领区的街头咖啡，非常的好喝。2022-01-18 10:05:094056024546710100208h-16h262000978269164307911
197BV18r4y1Q7sE533459953刘庸干净又卫生应广发粉丝的要求，印度晚上街头走一走，闲聊几句！2021-11-24 03:00:0441627611410338706300h-07h28500010800027000180009081
249BV1qS4y1o7Yk533459953刘庸干净又卫生快过年了，提前吃点好吃的。2022-01-19 11:00:1867943311498125761308h-16h4810004900020000160001642
\n
" + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the front of three videos by rank.\n", + "\n", + "df.query(\"1 <= rank <= 3\").sort_values([\"rank\", \"created\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "df_rank = df.query(\"rank != 0\")[\"rank\"]\n", + "df_rank.describe().to_markdown(\"~/Desktop/snippets.txt\", tablefmt=\"pretty\", colalign=(\"left\", ))" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:13.039774\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data=df_rank, kde=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "outputs": [ + { + "data": { + "text/plain": " rank rank_interval\n126 35 (20, 100]\n128 70 (20, 100]\n129 57 (20, 100]\n132 81 (20, 100]\n135 46 (20, 100]", + "text/html": "
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rankrank_interval
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" + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rank_interval = [(0, 3), (3, 10), (10, 20), (20, 100)]\n", + "bins = pd.IntervalIndex.from_tuples(rank_interval)\n", + "\n", + "counts = (\n", + " df_rank.to_frame()\n", + " .assign(\n", + " rank_interval = df[\"rank\"].pipe(pd.cut, bins=bins)\n", + " )\n", + ")\n", + "counts.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 241, + "outputs": [ + { + "data": { + "text/plain": "rank_interval\n(0, 3] 5\n(3, 10] 8\n(10, 20] 16\n(20, 100] 73\ndtype: int64" + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rank_interval_freq = counts.value_counts(\"rank_interval\", ascending=True)\n", + "rank_interval_freq.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 242, + "outputs": [ + { + "data": { + "text/plain": "[Text(0, 0, '5'), Text(0, 0, '8'), Text(0, 0, '16'), Text(0, 0, '73')]" + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:13.684526\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.countplot(x=\"rank_interval\", data=counts)\n", + "ax.bar_label(container=ax.containers[0], labels=rank_interval_freq.values)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### 播放量" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n156 BV1PQ4y167xk 533459953 刘庸干净又卫生 刘庸神作，万人要求的海盗狗。 \n135 BV1Lb4y127dj 533459953 刘庸干净又卫生 还是芦荟店的芦荟汁干净又卫生啊！ \n128 BV15b4y1U7kV 533459953 刘庸干净又卫生 刘庸继芦荟汁后又一重磅力作，水果捞！ \n251 BV1AY41187b3 533459953 刘庸干净又卫生 久等了，昏昏沉沉好几天，今天清醒多了。 \n129 BV1W44y187z5 533459953 刘庸干净又卫生 好久没来吃这玩意，这次我得休息几天？ \n\n created play comment video_review rank created_type \\\n156 2021-10-03 12:30:36 15653236 14163 8221 79 08h-16h \n135 2021-09-10 08:40:58 10565366 10196 21830 46 08h-16h \n128 2021-09-03 05:19:21 10029294 9371 33730 70 00h-07h \n251 2022-01-25 05:00:05 8986409 35247 82150 1 00h-07h \n129 2021-09-04 05:57:32 8674471 6814 12417 57 00h-07h \n\n like coin collect share seconds since_last_days \n156 439000 108000 89000 97000 33 2 \n135 327000 39000 20000 55000 132 2 \n128 277000 40000 20000 106000 123 1 \n251 1124000 350000 65000 48000 257 5 \n129 258000 29000 11000 25000 152 2 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
156BV1PQ4y167xk533459953刘庸干净又卫生刘庸神作，万人要求的海盗狗。2021-10-03 12:30:36156532361416382217908h-16h4390001080008900097000332
135BV1Lb4y127dj533459953刘庸干净又卫生还是芦荟店的芦荟汁干净又卫生啊！2021-09-10 08:40:581056536610196218304608h-16h3270003900020000550001322
128BV15b4y1U7kV533459953刘庸干净又卫生刘庸继芦荟汁后又一重磅力作，水果捞！2021-09-03 05:19:21100292949371337307000h-07h27700040000200001060001231
251BV1AY41187b3533459953刘庸干净又卫生久等了，昏昏沉沉好几天，今天清醒多了。2022-01-25 05:00:0589864093524782150100h-07h112400035000065000480002575
129BV1W44y187z5533459953刘庸干净又卫生好久没来吃这玩意，这次我得休息几天？2021-09-04 05:57:3286744716814124175700h-07h2580002900011000250001522
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" + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the TOP5 videos by playing.\n", + "\n", + "df.sort_values(\"play\", ascending=False).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n251 BV1AY41187b3 533459953 刘庸干净又卫生 久等了，昏昏沉沉好几天，今天清醒多了。 \n252 BV16F411H7gW 533459953 刘庸干净又卫生 历时8天，我康复了，谢谢所有的朋友们。 \n253 BV1vF411H7ZG 533459953 刘庸干净又卫生 除夕在印度买牛肉做大餐。 \n164 BV1oL41137tf 533459953 刘庸干净又卫生 第二部5+5=9 \n249 BV1qS4y1o7Yk 533459953 刘庸干净又卫生 快过年了，提前吃点好吃的。 \n\n created play comment video_review rank created_type \\\n251 2022-01-25 05:00:05 8986409 35247 82150 1 00h-07h \n252 2022-01-30 04:58:33 7348868 30466 66680 1 00h-07h \n253 2022-01-31 08:08:27 5668822 12304 37290 6 08h-16h \n164 2021-10-11 06:07:06 5234232 8771 5686 5 00h-07h \n249 2022-01-19 11:00:18 6794331 14981 25761 3 08h-16h \n\n like coin collect share seconds since_last_days \n251 1124000 350000 65000 48000 257 5 \n252 1011000 289000 60000 62000 105 5 \n253 576000 84000 25000 11000 213 2 \n164 491000 79000 40000 16000 70 2 \n249 481000 49000 20000 16000 164 2 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
251BV1AY41187b3533459953刘庸干净又卫生久等了，昏昏沉沉好几天，今天清醒多了。2022-01-25 05:00:0589864093524782150100h-07h112400035000065000480002575
252BV16F411H7gW533459953刘庸干净又卫生历时8天，我康复了，谢谢所有的朋友们。2022-01-30 04:58:3373488683046666680100h-07h101100028900060000620001055
253BV1vF411H7ZG533459953刘庸干净又卫生除夕在印度买牛肉做大餐。2022-01-31 08:08:2756688221230437290608h-16h5760008400025000110002132
164BV1oL41137tf533459953刘庸干净又卫生第二部5+5=92021-10-11 06:07:06523423287715686500h-07h491000790004000016000702
249BV1qS4y1o7Yk533459953刘庸干净又卫生快过年了，提前吃点好吃的。2022-01-19 11:00:1867943311498125761308h-16h4810004900020000160001642
\n
" + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(ops, ascending=False).head()" + ] + }, + { + "cell_type": "code", + "source": [ + "ax = (\n", + " df.loc[:, [\"bvid\", \"title\", *ops]]\n", + " .assign(\n", + " title=(\n", + " df[\"title\"].str.replace(r\"[！，。!.,]\", \"\\n\")\n", + " + df[\"bvid\"].map(lambda v: \"\\n({})\".format(v))\n", + " )\n", + " )\n", + " .sort_values(ops, ascending=False)\n", + " .head(5)\n", + " .set_index(\"title\")\n", + " .plot.barh(\n", + " figsize=(12, 12),\n", + " xlabel=\"\",\n", + " )\n", + ")\n", + "\n", + "# Revert display rank.\n", + "ax.invert_yaxis()\n", + "\n", + "# Annotation\n", + "for patch in ax.patches:\n", + " ax.text(\n", + " patch.get_width() + 0.3,\n", + " patch.get_y() + 0.1,\n", + " \" {:,}\".format(patch.get_width()),\n", + " fontsize=8,\n", + " color=\"black\",\n", + " )\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 245, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/ipykernel_7095/2474762560.py:5: FutureWarning: The default value of regex will change from True to False in a future version.\n", + " df[\"title\"].str.replace(r\"[！，。!.,]\", \"\\n\")\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:14.021411\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n251 BV1AY41187b3 533459953 刘庸干净又卫生 久等了，昏昏沉沉好几天，今天清醒多了。 \n252 BV16F411H7gW 533459953 刘庸干净又卫生 历时8天，我康复了，谢谢所有的朋友们。 \n197 BV18r4y1Q7sE 533459953 刘庸干净又卫生 应广发粉丝的要求，印度晚上街头走一走，闲聊几句！ \n253 BV1vF411H7ZG 533459953 刘庸干净又卫生 除夕在印度买牛肉做大餐。 \n128 BV15b4y1U7kV 533459953 刘庸干净又卫生 刘庸继芦荟汁后又一重磅力作，水果捞！ \n\n created play comment video_review rank created_type \\\n251 2022-01-25 05:00:05 8986409 35247 82150 1 00h-07h \n252 2022-01-30 04:58:33 7348868 30466 66680 1 00h-07h \n197 2021-11-24 03:00:04 4162761 14103 38706 3 00h-07h \n253 2022-01-31 08:08:27 5668822 12304 37290 6 08h-16h \n128 2021-09-03 05:19:21 10029294 9371 33730 70 00h-07h \n\n like coin collect share seconds since_last_days \n251 1124000 350000 65000 48000 257 5 \n252 1011000 289000 60000 62000 105 5 \n197 285000 108000 27000 18000 908 1 \n253 576000 84000 25000 11000 213 2 \n128 277000 40000 20000 106000 123 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
251BV1AY41187b3533459953刘庸干净又卫生久等了，昏昏沉沉好几天，今天清醒多了。2022-01-25 05:00:0589864093524782150100h-07h112400035000065000480002575
252BV16F411H7gW533459953刘庸干净又卫生历时8天，我康复了，谢谢所有的朋友们。2022-01-30 04:58:3373488683046666680100h-07h101100028900060000620001055
197BV18r4y1Q7sE533459953刘庸干净又卫生应广发粉丝的要求，印度晚上街头走一走，闲聊几句！2021-11-24 03:00:0441627611410338706300h-07h28500010800027000180009081
253BV1vF411H7ZG533459953刘庸干净又卫生除夕在印度买牛肉做大餐。2022-01-31 08:08:2756688221230437290608h-16h5760008400025000110002132
128BV15b4y1U7kV533459953刘庸干净又卫生刘庸继芦荟汁后又一重磅力作，水果捞！2021-09-03 05:19:21100292949371337307000h-07h27700040000200001060001231
\n
" + }, + "execution_count": 246, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the top 5 video sorted by danmu and comment.\n", + "\n", + "df.sort_values([\"video_review\", \"comment\"], ascending=False).head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 247, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/ipykernel_7095/3279381252.py:4: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " g.ax.set_xticklabels([f\"{int(n) / 1000:,.0f} K\" for n in g.ax.get_xticks()])\n", + "/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/ipykernel_7095/3279381252.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " g.ax.set_yticklabels([f\"{n:,.0f}\" for n in g.ax.get_yticks()])\n" + ] + }, + { + "data": { + "text/plain": "[Text(0, -20000.0, '-20,000'),\n Text(0, 0.0, '0'),\n Text(0, 20000.0, '20,000'),\n Text(0, 40000.0, '40,000'),\n Text(0, 60000.0, '60,000'),\n Text(0, 80000.0, '80,000'),\n Text(0, 100000.0, '100,000')]" + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:14.509138\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.lmplot(x=\"play\", y=\"video_review\", data=df, height=3)\n", + "\n", + "g.fig.set_size_inches(16, 8)\n", + "g.ax.set_xticklabels([f\"{int(n) / 1000:,.0f} K\" for n in g.ax.get_xticks()])\n", + "g.ax.set_yticklabels([f\"{n:,.0f}\" for n in g.ax.get_yticks()])\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 248, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:16.306803\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(\n", + " df.loc[:, \"created\":\"video_review\"]\n", + " .set_index(\"created\")\n", + " .plot.line(\n", + " figsize=(12, 10),\n", + " alpha=0.5,\n", + " x_compat=True,\n", + " xlabel=\"\",\n", + " subplots=True,\n", + " )\n", + ")\n", + "\n", + "plt.savefig(\"play-comment-danmu.png\", dpi=300)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:18.832473\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "\n", + "fig, ax = plt.subplots(3, 3, figsize=(12, 12))\n", + "\n", + "targets = [\n", + " \"play\", \"comment\", \"video_review\", \"seconds\", \"since_last_days\",\n", + " *ops,\n", + "]\n", + "\n", + "# combination like: (0, 0), (0, 1), (1, 0), (1, 1) etc.\n", + "locations = itertools.product(range(ax.shape[0]), range(ax.shape[1]))\n", + "\n", + "for i, j in locations:\n", + " position = i * 3 + j\n", + " sns.histplot(\n", + " x=df.filter(items=targets).iloc[:, position],\n", + " ax=ax[i][j],\n", + " kde=True,\n", + " alpha=0.5,\n", + " palette=\"crest\",\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 播放时长相关" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 250, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n136 BV1gg411c7AD 533459953 刘庸干净又卫生 乔治理发超长版，乔治买了新手机。 \n118 BV1fQ4y1Y7Nt 533459953 刘庸干净又卫生 乔治理发超长版，满足一些粉丝的要求。 \n148 BV1FM4y137pf 533459953 刘庸干净又卫生 终于可以睡个好觉了，失眠患者的福音，半个月一次的乔治理发又来了。 \n175 BV1Uv411M7s8 533459953 刘庸干净又卫生 失眠患者的福音，沉浸式体验，乔治理发超长版！ \n270 BV1WL4y1371r 533459953 刘庸干净又卫生 印度按摩完整版 \n204 BV1FL4y1W74f 533459953 刘庸干净又卫生 德里街头日常刮胡子 \n197 BV18r4y1Q7sE 533459953 刘庸干净又卫生 应广发粉丝的要求，印度晚上街头走一走，闲聊几句！ \n207 BV1pR4y1s7j2 533459953 刘庸干净又卫生 清真餐厅吃羊肉随拍。 \n169 BV1oq4y1V7KC 533459953 刘庸干净又卫生 进来一起互相陪伴吃个饭吧，一顿真实而普通的家常便饭。顺便聊个天。 \n238 BV1UR4y1u754 533459953 刘庸干净又卫生 分享日常早餐 \n\n created play comment video_review rank created_type \\\n136 2021-09-11 10:14:39 1317949 2412 7054 0 08h-16h \n118 2021-08-24 08:18:14 1267113 3415 9467 0 08h-16h \n148 2021-09-25 13:11:59 1747505 2502 6738 0 08h-16h \n175 2021-10-22 03:40:11 1188746 2349 7519 0 00h-07h \n270 2022-03-03 12:21:58 2189504 3863 12196 0 08h-16h \n204 2021-12-01 16:18:56 1471000 2577 4616 0 08h-16h \n197 2021-11-24 03:00:04 4162761 14103 38706 3 00h-07h \n207 2021-12-03 17:43:08 3394668 4983 13973 63 17h-23h \n169 2021-10-16 04:05:05 1816158 4793 12540 0 00h-07h \n238 2022-01-06 03:00:16 2400879 3782 12881 0 00h-07h \n\n like coin collect share seconds since_last_days \n136 80000 36000 11000 2041 2877 2 \n118 75000 29000 12000 2801 2678 2 \n148 71000 13000 7555 2157 2470 2 \n175 62000 11000 6489 1887 2056 1 \n270 117000 14000 15000 5957 1807 2 \n204 75000 4515 3569 990 922 2 \n197 285000 108000 27000 18000 908 1 \n207 160000 18000 5713 2751 706 1 \n169 121000 23000 4337 1417 642 1 \n238 130000 12000 3398 1472 603 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
136BV1gg411c7AD533459953刘庸干净又卫生乔治理发超长版，乔治买了新手机。2021-09-11 10:14:39131794924127054008h-16h800003600011000204128772
118BV1fQ4y1Y7Nt533459953刘庸干净又卫生乔治理发超长版，满足一些粉丝的要求。2021-08-24 08:18:14126711334159467008h-16h750002900012000280126782
148BV1FM4y137pf533459953刘庸干净又卫生终于可以睡个好觉了，失眠患者的福音，半个月一次的乔治理发又来了。2021-09-25 13:11:59174750525026738008h-16h71000130007555215724702
175BV1Uv411M7s8533459953刘庸干净又卫生失眠患者的福音，沉浸式体验，乔治理发超长版！2021-10-22 03:40:11118874623497519000h-07h62000110006489188720561
270BV1WL4y1371r533459953刘庸干净又卫生印度按摩完整版2022-03-03 12:21:582189504386312196008h-16h1170001400015000595718072
204BV1FL4y1W74f533459953刘庸干净又卫生德里街头日常刮胡子2021-12-01 16:18:56147100025774616008h-16h75000451535699909222
197BV18r4y1Q7sE533459953刘庸干净又卫生应广发粉丝的要求，印度晚上街头走一走，闲聊几句！2021-11-24 03:00:0441627611410338706300h-07h28500010800027000180009081
207BV1pR4y1s7j2533459953刘庸干净又卫生清真餐厅吃羊肉随拍。2021-12-03 17:43:0833946684983139736317h-23h16000018000571327517061
169BV1oq4y1V7KC533459953刘庸干净又卫生进来一起互相陪伴吃个饭吧，一顿真实而普通的家常便饭。顺便聊个天。2021-10-16 04:05:051816158479312540000h-07h12100023000433714176421
238BV1UR4y1u754533459953刘庸干净又卫生分享日常早餐2022-01-06 03:00:162400879378212881000h-07h13000012000339814726031
\n
" + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values([\"seconds\"], ascending=False).head(10)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 251, + "outputs": [], + "source": [ + "df[\"seconds\"].map(lambda v: v <= 1000 or False).value_counts().to_markdown(\"~/Desktop/snippets.txt\", tablefmt=\"pretty\", colalign=(\"left\", ))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 252, + "outputs": [ + { + "data": { + "text/plain": " bvid mid author title \\\n204 BV1FL4y1W74f 533459953 刘庸干净又卫生 德里街头日常刮胡子 \n197 BV18r4y1Q7sE 533459953 刘庸干净又卫生 应广发粉丝的要求，印度晚上街头走一走，闲聊几句！ \n207 BV1pR4y1s7j2 533459953 刘庸干净又卫生 清真餐厅吃羊肉随拍。 \n169 BV1oq4y1V7KC 533459953 刘庸干净又卫生 进来一起互相陪伴吃个饭吧，一顿真实而普通的家常便饭。顺便聊个天。 \n238 BV1UR4y1u754 533459953 刘庸干净又卫生 分享日常早餐 \n\n created play comment video_review rank created_type \\\n204 2021-12-01 16:18:56 1471000 2577 4616 0 08h-16h \n197 2021-11-24 03:00:04 4162761 14103 38706 3 00h-07h \n207 2021-12-03 17:43:08 3394668 4983 13973 63 17h-23h \n169 2021-10-16 04:05:05 1816158 4793 12540 0 00h-07h \n238 2022-01-06 03:00:16 2400879 3782 12881 0 00h-07h \n\n like coin collect share seconds since_last_days \n204 75000 4515 3569 990 922 2 \n197 285000 108000 27000 18000 908 1 \n207 160000 18000 5713 2751 706 1 \n169 121000 23000 4337 1417 642 1 \n238 130000 12000 3398 1472 603 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bvidmidauthortitlecreatedplaycommentvideo_reviewrankcreated_typelikecoincollectsharesecondssince_last_days
204BV1FL4y1W74f533459953刘庸干净又卫生德里街头日常刮胡子2021-12-01 16:18:56147100025774616008h-16h75000451535699909222
197BV18r4y1Q7sE533459953刘庸干净又卫生应广发粉丝的要求，印度晚上街头走一走，闲聊几句！2021-11-24 03:00:0441627611410338706300h-07h28500010800027000180009081
207BV1pR4y1s7j2533459953刘庸干净又卫生清真餐厅吃羊肉随拍。2021-12-03 17:43:0833946684983139736317h-23h16000018000571327517061
169BV1oq4y1V7KC533459953刘庸干净又卫生进来一起互相陪伴吃个饭吧，一顿真实而普通的家常便饭。顺便聊个天。2021-10-16 04:05:051816158479312540000h-07h12100023000433714176421
238BV1UR4y1u754533459953刘庸干净又卫生分享日常早餐2022-01-06 03:00:162400879378212881000h-07h13000012000339814726031
\n
" + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.query(\"seconds <= 1000\").sort_values([\"seconds\"], ascending=False).head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 253, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:21.497257\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "\n", + "import matplotlib.transforms as transforms\n", + "\n", + "base = (\n", + " df.filter(items=[\"created\", \"seconds\"])\n", + " .query(\"seconds <= 1000\")\n", + " .set_index(\"created\")\n", + ")\n", + "\n", + "ax = base.plot.line(\n", + " figsize=(12, 10),\n", + " x_compat=True,\n", + " alpha=0.5,\n", + " xlabel=\"\",\n", + " ylabel=\"Seconds(s)\",\n", + ")\n", + "\n", + "\n", + "(\n", + " base.rolling(7)\n", + " .mean()\n", + " .rename({\"seconds\": \"rolling seconds(per 7 days)\"}, axis=1)\n", + " .plot.line(ax=ax, color=\"red\", xlabel=\"\")\n", + ")\n", + "\n", + "# reference: https://stackoverflow.com/a/42879040\n", + "trans = transforms.blended_transform_factory(\n", + " ax.get_yticklabels()[0].get_transform(), ax.transData\n", + ")\n", + "\n", + "seconds_mean = df[\"seconds\"].median().round(2)\n", + "ax.axhline(y=seconds_mean, linestyle=\"--\", alpha=0.5, label=\"median seconds\", color=\"grey\")\n", + "ax.text(\n", + " x=0,\n", + " y=seconds_mean,\n", + " s=f\"{int(seconds_mean / 60)} min {math.ceil(seconds_mean % 60)} s\",\n", + " ha=\"right\",\n", + " va=\"center\",\n", + " transform=trans,\n", + ")\n", + "ax.legend()\n", + "plt.savefig(\"seconds-line-plot.png\", dpi=400)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 254, + "outputs": [ + { + "data": { + "text/plain": "created_type\n08h-16h 148\n17h-23h 9\n00h-07h 124\nName: bvid, dtype: int64" + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"created_type\")[\"bvid\"].count()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-04-10T16:22:22.674127\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.groupby(\"created_type\")[\"bvid\"].count().plot.pie(\n", + " autopct=\"%1.2f%%\",\n", + " figsize=(8, 8),\n", + " ylabel=\"\",\n", + " startangle=270,\n", + " explode=[0.01]*3,\n", + ")\n", + "plt.savefig(\"pie-chart.png\", dpi=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": " play comment video_review like coin\nplay 1.000000 0.628384 0.593064 0.735980 0.520579\ncomment 0.628384 1.000000 0.805082 0.788206 0.743240\nvideo_review 0.593064 0.805082 1.000000 0.826462 0.825229\nlike 0.735980 0.788206 0.826462 1.000000 0.813151\ncoin 0.520579 0.743240 0.825229 0.813151 1.000000", + "text/html": "
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playcommentvideo_reviewlikecoin
play1.0000000.6283840.5930640.7359800.520579
comment0.6283841.0000000.8050820.7882060.743240
video_review0.5930640.8050821.0000000.8264620.825229
like0.7359800.7882060.8264621.0000000.813151
coin0.5205790.7432400.8252290.8131511.000000
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" + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "targets = [\"created_type\", \"play\", \"comment\", \"video_review\", \"like\", \"coin\"]\n", + "df[targets].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Z-score\n", + "normalize = lambda col: (col - col.mean()) / col.std()\n", + "\n", + "normalized_df = (\n", + " df[targets]\n", + " .set_index(\"created_type\")\n", + " .transform(normalize)\n", + " .reset_index()\n", + ")\n", + "normalized_df.head().to_markdown(\"~/Desktop/snippets.txt\", tablefmt=\"pretty\", colalign=(\"left\", ))" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/seaborn/utils.py:95: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n", + " fig.canvas.draw()\n", + "/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/ipykernel_7095/4222612346.py:11: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n", + " plt.savefig(\"corr-matrix-plot.png\", dpi=400)\n", + "/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "text/plain": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sns.pairplot(\n", + " data=normalized_df,\n", + " hue=\"created_type\",\n", + " kind=\"reg\",\n", + " plot_kws={\n", + " \"line_kws\": {\"alpha\": 0.2},\n", + " \"scatter_kws\": {\"alpha\": 0.6},\n", + " }\n", + ")\n", + "\n", + "plt.savefig(\"corr-matrix-plot.png\", dpi=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "[('印度', 0.683859592143686),\n ('干净又卫生', 0.3195038085407572),\n ('甘蔗汁', 0.15681817260167039),\n ('芦荟汁', 0.14256197509242763),\n ('乔治', 0.12482552216675946),\n ('理发', 0.11240551405567928),\n ('刨冰', 0.09979338256469934),\n ('早餐', 0.08616520567403119),\n ('味道', 0.07351749903948775),\n ('小吃', 0.07328070809749444)]" + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import jieba\n", + "import jieba.analyse as ja\n", + "\n", + "jieba.add_word(\"干净又卫生\", tag=\"n\")\n", + "jieba.add_word(\"芦荟汁\", tag=\"n\")\n", + "\n", + "title = \"\\n\".join(df[\"title\"].tolist())\n", + "keywords = ja.extract_tags(\n", + " title,\n", + " topK=100,\n", + " withWeight=True,\n", + " allowPOS=(\"nr\", \"v\", \"n\", \"ns\", \"vn\"),\n", + ")\n", + "keywords[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pyecharts import options as opts\n", + "from pyecharts.charts import WordCloud\n", + "from pyecharts.globals import CurrentConfig, NotebookType\n", + "\n", + "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "new Promise(function(resolve, reject) {\n var script = document.createElement(\"script\");\n script.onload = resolve;\n script.onerror = reject;\n script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n document.head.appendChild(script);\n}).then(() => {\nnew Promise(function(resolve, reject) {\n var script = document.createElement(\"script\");\n script.onload = resolve;\n script.onerror = reject;\n script.src = \"https://assets.pyecharts.org/assets/echarts-wordcloud.min.js\";\n document.head.appendChild(script);\n}).then(() => {\n\n});\n});" + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graphics = (\n", + " WordCloud(\n", + " init_opts=opts.InitOpts(\n", + " height=\"800px\",\n", + " width=\"1000px\",\n", + " bg_color=\"white\",\n", + " )\n", + " )\n", + " .add(\"\", keywords, word_size_range=[25, 80])\n", + " .set_global_opts(\n", + " title_opts=opts.TitleOpts(\n", + " title=\"视频标题关键词 TOP 100\",\n", + " subtitle=\"UP 主：刘庸干净又卫生\",\n", + " pos_left=\"center\",\n", + " ),\n", + " toolbox_opts=opts.ToolboxOpts(\n", + " is_show=True,\n", + " pos_left=\"center\",\n", + " pos_top=\"8%\",\n", + " feature=opts.ToolBoxFeatureOpts(\n", + " opts.ToolBoxFeatureSaveAsImageOpts(\n", + " pixel_ratio=5,\n", + " background_color=\"#ffffff\",\n", + " )\n", + " )\n", + " ),\n", + " )\n", + ")\n", + "graphics.load_javascript()" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n\n \n\n\n
\n \n\n\n" + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graphics.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/data/bilibili.csv b/data/bilibili.csv new file mode 100644 index 0000000..b35231f --- /dev/null +++ b/data/bilibili.csv @@ -0,0 +1,282 @@ 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