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sspai-100-hours-series-python/code/02/main.ipynb
100gle e9cc3c297a feat: 更新引入案例相关源码
2022-04-19 20:57:50 +08:00

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{
"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": [
"<class 'pandas.core.frame.DataFrame'>\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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>comment</th>\n <th>typeid</th>\n <th>play</th>\n <th>subtitle</th>\n <th>copyright</th>\n <th>review</th>\n <th>mid</th>\n <th>created</th>\n <th>video_review</th>\n <th>aid</th>\n <th>is_pay</th>\n <th>is_union_video</th>\n <th>is_steins_gate</th>\n <th>is_live_playback</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>2.810000e+02</td>\n <td>0.0</td>\n <td>281.0</td>\n <td>281.0</td>\n <td>281.0</td>\n <td>2.810000e+02</td>\n <td>281.000000</td>\n <td>2.810000e+02</td>\n <td>281.0</td>\n <td>281.0</td>\n <td>281.0</td>\n <td>281.0</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>3587.989324</td>\n <td>168.017794</td>\n <td>2.768051e+06</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.632178e+09</td>\n <td>6372.619217</td>\n <td>5.872551e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>std</th>\n <td>4407.248268</td>\n <td>78.913400</td>\n <td>1.754950e+06</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>8.106261e+06</td>\n <td>8871.608323</td>\n <td>2.255457e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>min</th>\n <td>172.000000</td>\n <td>21.000000</td>\n <td>3.336690e+05</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.617385e+09</td>\n <td>51.000000</td>\n <td>2.049769e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>1286.000000</td>\n <td>212.000000</td>\n <td>1.648079e+06</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.625652e+09</td>\n <td>1430.000000</td>\n <td>3.806299e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>2577.000000</td>\n <td>212.000000</td>\n <td>2.435455e+06</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.631695e+09</td>\n <td>3514.000000</td>\n <td>5.904713e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>4639.000000</td>\n <td>212.000000</td>\n <td>3.415756e+06</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.638846e+09</td>\n <td>7998.000000</td>\n <td>7.637649e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>max</th>\n <td>46792.000000</td>\n <td>239.000000</td>\n <td>1.565324e+07</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>533459953.0</td>\n <td>1.648907e+09</td>\n <td>82150.000000</td>\n <td>9.778694e+08</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 224,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw.describe()"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": " pic description \\\ncount 281 279 \nunique 281 1 \ntop http://i2.hdslb.com/bfs/archive/550244aa3fb1e8... - \nfreq 1 279 \n\n title author length bvid like coin \\\ncount 281 281 281 281 279 279 \nunique 281 1 146 281 176 223 \ntop 印度阿姆利则降旗仪式，富有娱乐精神的表演。 刘庸干净又卫生 00:49 BV19r4y1r7wx 4.0万 1.2万 \nfreq 1 281 7 1 5 10 \n\n collect share rank \ncount 279 279 102 \nunique 262 260 68 \ntop 1.2万 1.1万 全站排行榜最高第19名 \nfreq 7 4 3 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>pic</th>\n <th>description</th>\n <th>title</th>\n <th>author</th>\n <th>length</th>\n <th>bvid</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>rank</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>281</td>\n <td>279</td>\n <td>281</td>\n <td>281</td>\n <td>281</td>\n <td>281</td>\n <td>279</td>\n <td>279</td>\n <td>279</td>\n <td>279</td>\n <td>102</td>\n </tr>\n <tr>\n <th>unique</th>\n <td>281</td>\n <td>1</td>\n <td>281</td>\n <td>1</td>\n <td>146</td>\n <td>281</td>\n <td>176</td>\n <td>223</td>\n <td>262</td>\n <td>260</td>\n <td>68</td>\n </tr>\n <tr>\n <th>top</th>\n <td>http://i2.hdslb.com/bfs/archive/550244aa3fb1e8...</td>\n <td>-</td>\n <td>印度阿姆利则降旗仪式，富有娱乐精神的表演。</td>\n <td>刘庸干净又卫生</td>\n <td>00:49</td>\n <td>BV19r4y1r7wx</td>\n <td>4.0万</td>\n <td>1.2万</td>\n <td>1.2万</td>\n <td>1.1万</td>\n <td>全站排行榜最高第19名</td>\n </tr>\n <tr>\n <th>freq</th>\n <td>1</td>\n <td>279</td>\n <td>1</td>\n <td>281</td>\n <td>7</td>\n <td>1</td>\n <td>5</td>\n <td>10</td>\n <td>7</td>\n <td>4</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>length</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n </tr>\n <tr>\n <th>bvid</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>BV19r4y1r7wx</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度阿姆利则降旗仪式，富有娱乐精神的表演。</td>\n <td>1636948887</td>\n <td>00:20</td>\n <td>1695848</td>\n <td>2073</td>\n <td>1050</td>\n <td>NaN</td>\n <td>7.9万</td>\n <td>2182</td>\n <td>2088</td>\n <td>1856</td>\n </tr>\n <tr>\n <th>BV14r4y1y7Ln</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>发烧怎么办？看开挂的民族如何应对！</td>\n <td>1634526240</td>\n <td>02:24</td>\n <td>2240231</td>\n <td>6026</td>\n <td>6124</td>\n <td>NaN</td>\n <td>9.5万</td>\n <td>5265</td>\n <td>3241</td>\n <td>8044</td>\n </tr>\n <tr>\n <th>BV1Rq4y1Z7TT</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。</td>\n <td>1631094362</td>\n <td>00:50</td>\n <td>2435455</td>\n <td>2875</td>\n <td>1766</td>\n <td>NaN</td>\n <td>10.4万</td>\n <td>8106</td>\n <td>2410</td>\n <td>1844</td>\n </tr>\n <tr>\n <th>BV1n54y1E7D5</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度新德里街头水果摊大全，堪比超市！</td>\n <td>1626083657</td>\n <td>00:59</td>\n <td>959967</td>\n <td>807</td>\n <td>1070</td>\n <td>NaN</td>\n <td>3.2万</td>\n <td>976</td>\n <td>658</td>\n <td>472</td>\n </tr>\n <tr>\n <th>BV1Yf4y1t7VV</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>这什么玩意儿？买了个寂寞！</td>\n <td>1624540361</td>\n <td>00:55</td>\n <td>2163298</td>\n <td>596</td>\n <td>853</td>\n <td>NaN</td>\n <td>5.1万</td>\n <td>1483</td>\n <td>1289</td>\n <td>481</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n </tr>\n <tr>\n <th>bvid</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>BV19r4y1r7wx</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度阿姆利则降旗仪式，富有娱乐精神的表演。</td>\n <td>2021-11-15 04:01:27</td>\n <td>1695848</td>\n <td>2073</td>\n <td>1050</td>\n <td>0</td>\n <td>00h-07h</td>\n </tr>\n <tr>\n <th>BV14r4y1y7Ln</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>发烧怎么办？看开挂的民族如何应对！</td>\n <td>2021-10-18 03:04:00</td>\n <td>2240231</td>\n <td>6026</td>\n <td>6124</td>\n <td>0</td>\n <td>00h-07h</td>\n </tr>\n <tr>\n <th>BV1Rq4y1Z7TT</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。</td>\n <td>2021-09-08 09:46:02</td>\n <td>2435455</td>\n <td>2875</td>\n <td>1766</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n <tr>\n <th>BV1n54y1E7D5</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度新德里街头水果摊大全，堪比超市！</td>\n <td>2021-07-12 09:54:17</td>\n <td>959967</td>\n <td>807</td>\n <td>1070</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n <tr>\n <th>BV1Yf4y1t7VV</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>这什么玩意儿？买了个寂寞！</td>\n <td>2021-06-24 13:12:41</td>\n <td>2163298</td>\n <td>596</td>\n <td>853</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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<rank>\\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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n </tr>\n <tr>\n <th>bvid</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>BV19r4y1r7wx</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度阿姆利则降旗仪式，富有娱乐精神的表演。</td>\n <td>2021-11-15 04:01:27</td>\n <td>1695848</td>\n <td>2073</td>\n <td>1050</td>\n <td>0</td>\n <td>00h-07h</td>\n </tr>\n <tr>\n <th>BV14r4y1y7Ln</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>发烧怎么办？看开挂的民族如何应对！</td>\n <td>2021-10-18 03:04:00</td>\n <td>2240231</td>\n <td>6026</td>\n <td>6124</td>\n <td>0</td>\n <td>00h-07h</td>\n </tr>\n <tr>\n <th>BV1Rq4y1Z7TT</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>依旧是甘蔗汁，天太热，如果没有糖分补充就会昏倒。</td>\n <td>2021-09-08 09:46:02</td>\n <td>2435455</td>\n <td>2875</td>\n <td>1766</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n <tr>\n <th>BV1n54y1E7D5</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度新德里街头水果摊大全，堪比超市！</td>\n <td>2021-07-12 09:54:17</td>\n <td>959967</td>\n <td>807</td>\n <td>1070</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n <tr>\n <th>BV1Yf4y1t7VV</th>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>这什么玩意儿？买了个寂寞！</td>\n <td>2021-06-24 13:12:41</td>\n <td>2163298</td>\n <td>596</td>\n <td>853</td>\n <td>0</td>\n <td>08h-16h</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n </tr>\n <tr>\n <th>bvid</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>BV19r4y1r7wx</th>\n <td>79000</td>\n <td>2182</td>\n <td>2088</td>\n <td>1856</td>\n </tr>\n <tr>\n <th>BV14r4y1y7Ln</th>\n <td>95000</td>\n <td>5265</td>\n <td>3241</td>\n <td>8044</td>\n </tr>\n <tr>\n <th>BV1Rq4y1Z7TT</th>\n <td>104000</td>\n <td>8106</td>\n <td>2410</td>\n <td>1844</td>\n </tr>\n <tr>\n <th>BV1n54y1E7D5</th>\n <td>32000</td>\n <td>976</td>\n <td>658</td>\n <td>472</td>\n </tr>\n <tr>\n <th>BV1Yf4y1t7VV</th>\n <td>51000</td>\n <td>1483</td>\n <td>1289</td>\n <td>481</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>seconds</th>\n </tr>\n <tr>\n <th>bvid</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>BV19r4y1r7wx</th>\n <td>20</td>\n </tr>\n <tr>\n <th>BV14r4y1y7Ln</th>\n <td>144</td>\n </tr>\n <tr>\n <th>BV1Rq4y1Z7TT</th>\n <td>50</td>\n </tr>\n <tr>\n <th>BV1n54y1E7D5</th>\n <td>59</td>\n </tr>\n <tr>\n <th>BV1Yf4y1t7VV</th>\n <td>55</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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<minute>\\d{2}):(?P<second>\\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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>BV1Rh411D755</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>原创首发</td>\n <td>2021-04-02 17:31:29</td>\n <td>1769938</td>\n <td>2570</td>\n <td>4809</td>\n <td>0</td>\n <td>17h-23h</td>\n <td>65000</td>\n <td>6296</td>\n <td>7349</td>\n <td>3728</td>\n <td>277</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>BV175411A7L3</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>喝一个养颜美容芦荟汁</td>\n <td>2021-04-05 12:46:03</td>\n <td>1865198</td>\n <td>1802</td>\n <td>5065</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>70000</td>\n <td>16000</td>\n <td>9947</td>\n <td>17000</td>\n <td>131</td>\n <td>3</td>\n </tr>\n <tr>\n <th>2</th>\n <td>BV1jK4y1m7Dc</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>刷刷鞋</td>\n <td>2021-04-06 17:00:02</td>\n <td>761236</td>\n <td>311</td>\n <td>535</td>\n <td>0</td>\n <td>17h-23h</td>\n <td>16000</td>\n <td>509</td>\n <td>503</td>\n <td>120</td>\n <td>85</td>\n <td>2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>BV1M54y1b7tD</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>我知道你们早就看过了，我只是怀念一下而已。</td>\n <td>2021-04-07 15:35:02</td>\n <td>2190407</td>\n <td>919</td>\n <td>1017</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>53000</td>\n <td>5952</td>\n <td>6343</td>\n <td>1476</td>\n <td>59</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BV1nb4y1D77D</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>天热买个冰棍儿</td>\n <td>2021-04-08 16:49:32</td>\n <td>1682582</td>\n <td>453</td>\n <td>956</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>42000</td>\n <td>1322</td>\n <td>1393</td>\n <td>519</td>\n <td>54</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>281</td>\n <td>2.810000e+02</td>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>2.810000e+02</td>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>281.000000</td>\n <td>281.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>2021-09-20 22:42:29.647686656</td>\n <td>2.768051e+06</td>\n <td>3587.989324</td>\n <td>6372.619217</td>\n <td>14.103203</td>\n <td>1.346215e+05</td>\n <td>13328.181495</td>\n <td>5659.747331</td>\n <td>6202.003559</td>\n <td>155.227758</td>\n <td>1.782918</td>\n </tr>\n <tr>\n <th>min</th>\n <td>2021-04-02 17:31:29</td>\n <td>3.336690e+05</td>\n <td>172.000000</td>\n <td>51.000000</td>\n <td>0.000000</td>\n <td>0.000000e+00</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>9.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>2021-09-15 08:43:32</td>\n <td>2.435455e+06</td>\n <td>2577.000000</td>\n <td>3514.000000</td>\n <td>0.000000</td>\n <td>9.500000e+04</td>\n <td>5839.000000</td>\n <td>3398.000000</td>\n <td>2751.000000</td>\n <td>76.000000</td>\n <td>2.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>2022-04-02 13:40:04</td>\n <td>1.565324e+07</td>\n <td>46792.000000</td>\n <td>82150.000000</td>\n <td>91.000000</td>\n <td>1.124000e+06</td>\n <td>350000.000000</td>\n <td>89000.000000</td>\n <td>106000.000000</td>\n <td>2877.000000</td>\n <td>7.000000</td>\n </tr>\n <tr>\n <th>std</th>\n <td>NaN</td>\n <td>1.754950e+06</td>\n <td>4407.248268</td>\n <td>8871.608323</td>\n <td>23.908158</td>\n <td>1.282603e+05</td>\n <td>30758.026709</td>\n <td>8698.436615</td>\n <td>12144.340569</td>\n <td>327.588938</td>\n <td>0.909784</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>184</th>\n <td>BV16S4y1R7Vd</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>出门吃个简易早餐，经济又实惠，干净又卫生！</td>\n <td>2021-11-02 03:30:18</td>\n <td>3852133</td>\n <td>4766</td>\n <td>9329</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>209</td>\n <td>1</td>\n </tr>\n <tr>\n <th>214</th>\n <td>BV1QQ4y1e7dB</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度街头吃点烧烤！</td>\n <td>2021-12-11 03:00:10</td>\n <td>3867097</td>\n <td>6189</td>\n <td>29091</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>203</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>251</th>\n <td>BV1AY41187b3</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>久等了，昏昏沉沉好几天，今天清醒多了。</td>\n <td>2022-01-25 05:00:05</td>\n <td>8986409</td>\n <td>35247</td>\n <td>82150</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1124000</td>\n <td>350000</td>\n <td>65000</td>\n <td>48000</td>\n <td>257</td>\n <td>5</td>\n </tr>\n <tr>\n <th>252</th>\n <td>BV16F411H7gW</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>历时8天，我康复了，谢谢所有的朋友们。</td>\n <td>2022-01-30 04:58:33</td>\n <td>7348868</td>\n <td>30466</td>\n <td>66680</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1011000</td>\n <td>289000</td>\n <td>60000</td>\n <td>62000</td>\n <td>105</td>\n <td>5</td>\n </tr>\n <tr>\n <th>248</th>\n <td>BV1Lr4y1e7WH</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度白领区的街头咖啡，非常的好喝。</td>\n <td>2022-01-18 10:05:09</td>\n <td>4056024</td>\n <td>5467</td>\n <td>10100</td>\n <td>2</td>\n <td>08h-16h</td>\n <td>262000</td>\n <td>9782</td>\n <td>6916</td>\n <td>4307</td>\n <td>91</td>\n <td>1</td>\n </tr>\n <tr>\n <th>197</th>\n <td>BV18r4y1Q7sE</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>应广发粉丝的要求，印度晚上街头走一走，闲聊几句！</td>\n <td>2021-11-24 03:00:04</td>\n <td>4162761</td>\n <td>14103</td>\n <td>38706</td>\n <td>3</td>\n <td>00h-07h</td>\n <td>285000</td>\n <td>108000</td>\n <td>27000</td>\n <td>18000</td>\n <td>908</td>\n <td>1</td>\n </tr>\n <tr>\n <th>249</th>\n <td>BV1qS4y1o7Yk</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>快过年了，提前吃点好吃的。</td>\n <td>2022-01-19 11:00:18</td>\n <td>6794331</td>\n <td>14981</td>\n <td>25761</td>\n <td>3</td>\n <td>08h-16h</td>\n <td>481000</td>\n <td>49000</td>\n <td>20000</td>\n <td>16000</td>\n <td>164</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<AxesSubplot:xlabel='rank', ylabel='Count'>"
},
"execution_count": 239,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"metadata": {},
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}
],
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>rank</th>\n <th>rank_interval</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>126</th>\n <td>35</td>\n <td>(20, 100]</td>\n </tr>\n <tr>\n <th>128</th>\n <td>70</td>\n <td>(20, 100]</td>\n </tr>\n <tr>\n <th>129</th>\n <td>57</td>\n <td>(20, 100]</td>\n </tr>\n <tr>\n <th>132</th>\n <td>81</td>\n <td>(20, 100]</td>\n </tr>\n <tr>\n <th>135</th>\n <td>46</td>\n <td>(20, 100]</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 240,
"metadata": {},
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}
],
"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()"
],
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"collapsed": false,
"pycharm": {
"name": "#%%\n"
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}
},
{
"cell_type": "code",
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"outputs": [
{
"data": {
"text/plain": "[Text(0, 0, '5'), Text(0, 0, '8'), Text(0, 0, '16'), Text(0, 0, '73')]"
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"execution_count": 242,
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},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>156</th>\n <td>BV1PQ4y167xk</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>刘庸神作，万人要求的海盗狗。</td>\n <td>2021-10-03 12:30:36</td>\n <td>15653236</td>\n <td>14163</td>\n <td>8221</td>\n <td>79</td>\n <td>08h-16h</td>\n <td>439000</td>\n <td>108000</td>\n <td>89000</td>\n <td>97000</td>\n <td>33</td>\n <td>2</td>\n </tr>\n <tr>\n <th>135</th>\n <td>BV1Lb4y127dj</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>还是芦荟店的芦荟汁干净又卫生啊！</td>\n <td>2021-09-10 08:40:58</td>\n <td>10565366</td>\n <td>10196</td>\n <td>21830</td>\n <td>46</td>\n <td>08h-16h</td>\n <td>327000</td>\n <td>39000</td>\n <td>20000</td>\n <td>55000</td>\n <td>132</td>\n <td>2</td>\n </tr>\n <tr>\n <th>128</th>\n <td>BV15b4y1U7kV</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>刘庸继芦荟汁后又一重磅力作，水果捞！</td>\n <td>2021-09-03 05:19:21</td>\n <td>10029294</td>\n <td>9371</td>\n <td>33730</td>\n <td>70</td>\n <td>00h-07h</td>\n <td>277000</td>\n <td>40000</td>\n <td>20000</td>\n <td>106000</td>\n <td>123</td>\n <td>1</td>\n </tr>\n <tr>\n <th>251</th>\n <td>BV1AY41187b3</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>久等了，昏昏沉沉好几天，今天清醒多了。</td>\n <td>2022-01-25 05:00:05</td>\n <td>8986409</td>\n <td>35247</td>\n <td>82150</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1124000</td>\n <td>350000</td>\n <td>65000</td>\n <td>48000</td>\n <td>257</td>\n <td>5</td>\n </tr>\n <tr>\n <th>129</th>\n <td>BV1W44y187z5</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>好久没来吃这玩意，这次我得休息几天？</td>\n <td>2021-09-04 05:57:32</td>\n <td>8674471</td>\n <td>6814</td>\n <td>12417</td>\n <td>57</td>\n <td>00h-07h</td>\n <td>258000</td>\n <td>29000</td>\n <td>11000</td>\n <td>25000</td>\n <td>152</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>251</th>\n <td>BV1AY41187b3</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>久等了，昏昏沉沉好几天，今天清醒多了。</td>\n <td>2022-01-25 05:00:05</td>\n <td>8986409</td>\n <td>35247</td>\n <td>82150</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1124000</td>\n <td>350000</td>\n <td>65000</td>\n <td>48000</td>\n <td>257</td>\n <td>5</td>\n </tr>\n <tr>\n <th>252</th>\n <td>BV16F411H7gW</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>历时8天，我康复了，谢谢所有的朋友们。</td>\n <td>2022-01-30 04:58:33</td>\n <td>7348868</td>\n <td>30466</td>\n <td>66680</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1011000</td>\n <td>289000</td>\n <td>60000</td>\n <td>62000</td>\n <td>105</td>\n <td>5</td>\n </tr>\n <tr>\n <th>253</th>\n <td>BV1vF411H7ZG</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>除夕在印度买牛肉做大餐。</td>\n <td>2022-01-31 08:08:27</td>\n <td>5668822</td>\n <td>12304</td>\n <td>37290</td>\n <td>6</td>\n <td>08h-16h</td>\n <td>576000</td>\n <td>84000</td>\n <td>25000</td>\n <td>11000</td>\n <td>213</td>\n <td>2</td>\n </tr>\n <tr>\n <th>164</th>\n <td>BV1oL41137tf</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>第二部5+5=9</td>\n <td>2021-10-11 06:07:06</td>\n <td>5234232</td>\n <td>8771</td>\n <td>5686</td>\n <td>5</td>\n <td>00h-07h</td>\n <td>491000</td>\n <td>79000</td>\n <td>40000</td>\n <td>16000</td>\n <td>70</td>\n <td>2</td>\n </tr>\n <tr>\n <th>249</th>\n <td>BV1qS4y1o7Yk</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>快过年了，提前吃点好吃的。</td>\n <td>2022-01-19 11:00:18</td>\n <td>6794331</td>\n <td>14981</td>\n <td>25761</td>\n <td>3</td>\n <td>08h-16h</td>\n <td>481000</td>\n <td>49000</td>\n <td>20000</td>\n <td>16000</td>\n <td>164</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<Figure size 864x864 with 1 Axes>",
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},
{
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>251</th>\n <td>BV1AY41187b3</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>久等了，昏昏沉沉好几天，今天清醒多了。</td>\n <td>2022-01-25 05:00:05</td>\n <td>8986409</td>\n <td>35247</td>\n <td>82150</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1124000</td>\n <td>350000</td>\n <td>65000</td>\n <td>48000</td>\n <td>257</td>\n <td>5</td>\n </tr>\n <tr>\n <th>252</th>\n <td>BV16F411H7gW</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>历时8天，我康复了，谢谢所有的朋友们。</td>\n <td>2022-01-30 04:58:33</td>\n <td>7348868</td>\n <td>30466</td>\n <td>66680</td>\n <td>1</td>\n <td>00h-07h</td>\n <td>1011000</td>\n <td>289000</td>\n <td>60000</td>\n <td>62000</td>\n <td>105</td>\n <td>5</td>\n </tr>\n <tr>\n <th>197</th>\n <td>BV18r4y1Q7sE</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>应广发粉丝的要求，印度晚上街头走一走，闲聊几句！</td>\n <td>2021-11-24 03:00:04</td>\n <td>4162761</td>\n <td>14103</td>\n <td>38706</td>\n <td>3</td>\n <td>00h-07h</td>\n <td>285000</td>\n <td>108000</td>\n <td>27000</td>\n <td>18000</td>\n <td>908</td>\n <td>1</td>\n </tr>\n <tr>\n <th>253</th>\n <td>BV1vF411H7ZG</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>除夕在印度买牛肉做大餐。</td>\n <td>2022-01-31 08:08:27</td>\n <td>5668822</td>\n <td>12304</td>\n <td>37290</td>\n <td>6</td>\n <td>08h-16h</td>\n <td>576000</td>\n <td>84000</td>\n <td>25000</td>\n <td>11000</td>\n <td>213</td>\n <td>2</td>\n </tr>\n <tr>\n <th>128</th>\n <td>BV15b4y1U7kV</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>刘庸继芦荟汁后又一重磅力作，水果捞！</td>\n <td>2021-09-03 05:19:21</td>\n <td>10029294</td>\n <td>9371</td>\n <td>33730</td>\n <td>70</td>\n <td>00h-07h</td>\n <td>277000</td>\n <td>40000</td>\n <td>20000</td>\n <td>106000</td>\n <td>123</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 246,
"metadata": {},
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}
],
"source": [
"# the top 5 video sorted by danmu and comment.\n",
"\n",
"df.sort_values([\"video_review\", \"comment\"], ascending=False).head()"
],
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"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"
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xlink:href=\"#STSongti-SC-Black-2c\" x=\"46.899994\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"68.799988\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"115.699982\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"162.599976\"/>\n <use xlink:href=\"#STSongti-SC-Black-20\" x=\"209.499969\"/>\n <use xlink:href=\"#STSongti-SC-Black-4b\" x=\"234.499954\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_3\">\n <g id=\"line2d_3\">\n <path d=\"M 256.112852 427.246667 \nL 256.112852 7.2 \n\" clip-path=\"url(#pa7bb745fe9)\" style=\"fill: none; stroke: #ffffff; stroke-linecap: round\"/>\n </g>\n <g id=\"text_3\">\n <!-- 4,000 K -->\n <g style=\"fill: #262626\" transform=\"translate(239.099806 444.666667)scale(0.11 -0.11)\">\n <defs>\n <path id=\"STSongti-SC-Black-34\" d=\"M 2086 4448 \nL 2387 4448 \nL 2387 1843 \nL 2918 1843 \nL 2918 1434 \nL 2387 1434 \nL 2387 282 \nL 1670 282 \nL 1670 1434 \nL 122 1434 \nL 122 1760 \nL 966 2912 \nQ 1254 3296 1529 3680 \nQ 1805 4064 2086 4448 \nz\nM 1670 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d=\"M 2458 4467 \nL 2509 4346 \nQ 2138 4141 1846 3926 \nQ 1555 3712 1356 3430 \nQ 1158 3149 1056 2761 \nQ 954 2374 954 1824 \nQ 954 1645 973 1414 \nQ 992 1184 1053 976 \nQ 1114 768 1235 627 \nQ 1357 486 1562 486 \nQ 1824 486 1987 739 \nQ 2150 992 2150 1562 \nQ 2150 2042 1996 2282 \nQ 1843 2522 1555 2522 \nQ 1434 2522 1318 2470 \nL 1267 2637 \nQ 1370 2682 1482 2704 \nQ 1594 2726 1747 2726 \nQ 2278 2726 2560 2425 \nQ 2842 2125 2842 1626 \nQ 2842 1318 2736 1072 \nQ 2630 826 2451 650 \nQ 2272 474 2035 378 \nQ 1798 282 1530 282 \nQ 1190 282 944 403 \nQ 698 525 534 739 \nQ 371 954 294 1242 \nQ 218 1530 218 1862 \nQ 218 2426 394 2864 \nQ 570 3302 874 3622 \nQ 1178 3942 1587 4150 \nQ 1997 4358 2458 4467 \nz\n\" transform=\"scale(0.015625)\"/>\n </defs>\n <use xlink:href=\"#STSongti-SC-Black-36\"/>\n <use xlink:href=\"#STSongti-SC-Black-2c\" x=\"46.899994\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"68.799988\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"115.699982\"/>\n <use 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1434 \nQ 2771 1190 2665 979 \nQ 2560 768 2384 614 \nQ 2208 461 1971 371 \nQ 1734 282 1466 282 \nQ 1184 282 950 362 \nQ 717 442 550 576 \nQ 384 710 294 892 \nQ 205 1075 205 1280 \nQ 205 1434 246 1584 \nQ 288 1734 390 1875 \nQ 493 2016 659 2137 \nQ 826 2259 1069 2368 \nz\nM 1683 2707 \nQ 1920 2854 2022 3046 \nQ 2125 3238 2125 3526 \nQ 2125 3827 1968 4019 \nQ 1811 4211 1472 4211 \nQ 1158 4211 1011 4035 \nQ 864 3859 864 3597 \nQ 864 3334 1046 3126 \nQ 1229 2918 1683 2707 \nz\nM 1254 2266 \nQ 1107 2176 1011 2077 \nQ 915 1978 860 1866 \nQ 806 1754 784 1622 \nQ 762 1491 762 1331 \nQ 762 941 976 710 \nQ 1190 480 1517 480 \nQ 2163 480 2163 1235 \nQ 2163 1414 2108 1555 \nQ 2054 1696 1942 1817 \nQ 1830 1939 1657 2048 \nQ 1485 2157 1254 2266 \nz\n\" transform=\"scale(0.015625)\"/>\n </defs>\n <use xlink:href=\"#STSongti-SC-Black-38\"/>\n <use xlink:href=\"#STSongti-SC-Black-2c\" x=\"46.899994\"/>\n <use xlink:href=\"#STSongti-SC-Black-30\" x=\"68.799988\"/>\n <use 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"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": {
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},
"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": {
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},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>136</th>\n <td>BV1gg411c7AD</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>乔治理发超长版，乔治买了新手机。</td>\n <td>2021-09-11 10:14:39</td>\n <td>1317949</td>\n <td>2412</td>\n <td>7054</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>80000</td>\n <td>36000</td>\n <td>11000</td>\n <td>2041</td>\n <td>2877</td>\n <td>2</td>\n </tr>\n <tr>\n <th>118</th>\n <td>BV1fQ4y1Y7Nt</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>乔治理发超长版，满足一些粉丝的要求。</td>\n <td>2021-08-24 08:18:14</td>\n <td>1267113</td>\n <td>3415</td>\n <td>9467</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>75000</td>\n <td>29000</td>\n <td>12000</td>\n <td>2801</td>\n <td>2678</td>\n <td>2</td>\n </tr>\n <tr>\n <th>148</th>\n <td>BV1FM4y137pf</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>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。</td>\n <td>2021-09-25 13:11:59</td>\n <td>1747505</td>\n <td>2502</td>\n <td>6738</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>71000</td>\n <td>13000</td>\n <td>7555</td>\n <td>2157</td>\n <td>2470</td>\n <td>2</td>\n </tr>\n <tr>\n <th>175</th>\n <td>BV1Uv411M7s8</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>失眠患者的福音，沉浸式体验，乔治理发超长版！</td>\n <td>2021-10-22 03:40:11</td>\n <td>1188746</td>\n <td>2349</td>\n <td>7519</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>62000</td>\n <td>11000</td>\n <td>6489</td>\n <td>1887</td>\n <td>2056</td>\n <td>1</td>\n </tr>\n <tr>\n <th>270</th>\n <td>BV1WL4y1371r</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>印度按摩完整版</td>\n <td>2022-03-03 12:21:58</td>\n <td>2189504</td>\n <td>3863</td>\n <td>12196</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>117000</td>\n <td>14000</td>\n <td>15000</td>\n <td>5957</td>\n <td>1807</td>\n <td>2</td>\n </tr>\n <tr>\n <th>204</th>\n <td>BV1FL4y1W74f</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>德里街头日常刮胡子</td>\n <td>2021-12-01 16:18:56</td>\n <td>1471000</td>\n <td>2577</td>\n <td>4616</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>75000</td>\n <td>4515</td>\n <td>3569</td>\n <td>990</td>\n <td>922</td>\n <td>2</td>\n </tr>\n <tr>\n <th>197</th>\n <td>BV18r4y1Q7sE</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>应广发粉丝的要求，印度晚上街头走一走，闲聊几句！</td>\n <td>2021-11-24 03:00:04</td>\n <td>4162761</td>\n <td>14103</td>\n <td>38706</td>\n <td>3</td>\n <td>00h-07h</td>\n <td>285000</td>\n <td>108000</td>\n <td>27000</td>\n <td>18000</td>\n <td>908</td>\n <td>1</td>\n </tr>\n <tr>\n <th>207</th>\n <td>BV1pR4y1s7j2</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>清真餐厅吃羊肉随拍。</td>\n <td>2021-12-03 17:43:08</td>\n <td>3394668</td>\n <td>4983</td>\n <td>13973</td>\n <td>63</td>\n <td>17h-23h</td>\n <td>160000</td>\n <td>18000</td>\n <td>5713</td>\n <td>2751</td>\n <td>706</td>\n <td>1</td>\n </tr>\n <tr>\n <th>169</th>\n <td>BV1oq4y1V7KC</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>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。</td>\n <td>2021-10-16 04:05:05</td>\n <td>1816158</td>\n <td>4793</td>\n <td>12540</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>121000</td>\n <td>23000</td>\n <td>4337</td>\n <td>1417</td>\n <td>642</td>\n <td>1</td>\n </tr>\n <tr>\n <th>238</th>\n <td>BV1UR4y1u754</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>分享日常早餐</td>\n <td>2022-01-06 03:00:16</td>\n <td>2400879</td>\n <td>3782</td>\n <td>12881</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>130000</td>\n <td>12000</td>\n <td>3398</td>\n <td>1472</td>\n <td>603</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 250,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values([\"seconds\"], ascending=False).head(10)"
],
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"collapsed": false,
"pycharm": {
"name": "#%%\n"
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}
},
{
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>bvid</th>\n <th>mid</th>\n <th>author</th>\n <th>title</th>\n <th>created</th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>rank</th>\n <th>created_type</th>\n <th>like</th>\n <th>coin</th>\n <th>collect</th>\n <th>share</th>\n <th>seconds</th>\n <th>since_last_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>204</th>\n <td>BV1FL4y1W74f</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>德里街头日常刮胡子</td>\n <td>2021-12-01 16:18:56</td>\n <td>1471000</td>\n <td>2577</td>\n <td>4616</td>\n <td>0</td>\n <td>08h-16h</td>\n <td>75000</td>\n <td>4515</td>\n <td>3569</td>\n <td>990</td>\n <td>922</td>\n <td>2</td>\n </tr>\n <tr>\n <th>197</th>\n <td>BV18r4y1Q7sE</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>应广发粉丝的要求，印度晚上街头走一走，闲聊几句！</td>\n <td>2021-11-24 03:00:04</td>\n <td>4162761</td>\n <td>14103</td>\n <td>38706</td>\n <td>3</td>\n <td>00h-07h</td>\n <td>285000</td>\n <td>108000</td>\n <td>27000</td>\n <td>18000</td>\n <td>908</td>\n <td>1</td>\n </tr>\n <tr>\n <th>207</th>\n <td>BV1pR4y1s7j2</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>清真餐厅吃羊肉随拍。</td>\n <td>2021-12-03 17:43:08</td>\n <td>3394668</td>\n <td>4983</td>\n <td>13973</td>\n <td>63</td>\n <td>17h-23h</td>\n <td>160000</td>\n <td>18000</td>\n <td>5713</td>\n <td>2751</td>\n <td>706</td>\n <td>1</td>\n </tr>\n <tr>\n <th>169</th>\n <td>BV1oq4y1V7KC</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>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。</td>\n <td>2021-10-16 04:05:05</td>\n <td>1816158</td>\n <td>4793</td>\n <td>12540</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>121000</td>\n <td>23000</td>\n <td>4337</td>\n <td>1417</td>\n <td>642</td>\n <td>1</td>\n </tr>\n <tr>\n <th>238</th>\n <td>BV1UR4y1u754</td>\n <td>533459953</td>\n <td>刘庸干净又卫生</td>\n <td>分享日常早餐</td>\n <td>2022-01-06 03:00:16</td>\n <td>2400879</td>\n <td>3782</td>\n <td>12881</td>\n <td>0</td>\n <td>00h-07h</td>\n <td>130000</td>\n <td>12000</td>\n <td>3398</td>\n <td>1472</td>\n <td>603</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": {
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xlink:href=\"#STSongti-SC-Black-20\" x=\"828.499786\"/>\n <use xlink:href=\"#STSongti-SC-Black-64\" x=\"853.499771\"/>\n <use xlink:href=\"#STSongti-SC-Black-61\" x=\"905.399765\"/>\n <use xlink:href=\"#STSongti-SC-Black-79\" x=\"954.699753\"/>\n <use xlink:href=\"#STSongti-SC-Black-73\" x=\"1000.599747\"/>\n <use xlink:href=\"#STSongti-SC-Black-29\" x=\"1036.499741\"/>\n </g>\n </g>\n <g id=\"line2d_17\">\n <path d=\"M 577.496406 51.377031 \nL 588.496406 51.377031 \nL 599.496406 51.377031 \n\" style=\"fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #808080; stroke-opacity: 0.5; stroke-width: 1.5\"/>\n </g>\n <g id=\"text_16\">\n <!-- median seconds -->\n <g style=\"fill: #262626\" transform=\"translate(608.296406 55.227031)scale(0.11 -0.11)\">\n <use xlink:href=\"#STSongti-SC-Black-6d\"/>\n <use xlink:href=\"#STSongti-SC-Black-65\" x=\"77.799988\"/>\n <use xlink:href=\"#STSongti-SC-Black-64\" x=\"120.199982\"/>\n <use xlink:href=\"#STSongti-SC-Black-69\" 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},
"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": "<Figure size 576x576 with 1 Axes>",
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},
"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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>play</th>\n <th>comment</th>\n <th>video_review</th>\n <th>like</th>\n <th>coin</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>play</th>\n <td>1.000000</td>\n <td>0.628384</td>\n <td>0.593064</td>\n <td>0.735980</td>\n <td>0.520579</td>\n </tr>\n <tr>\n <th>comment</th>\n <td>0.628384</td>\n <td>1.000000</td>\n <td>0.805082</td>\n <td>0.788206</td>\n <td>0.743240</td>\n </tr>\n <tr>\n <th>video_review</th>\n <td>0.593064</td>\n <td>0.805082</td>\n <td>1.000000</td>\n <td>0.826462</td>\n <td>0.825229</td>\n </tr>\n <tr>\n <th>like</th>\n <td>0.735980</td>\n <td>0.788206</td>\n <td>0.826462</td>\n <td>1.000000</td>\n <td>0.813151</td>\n </tr>\n <tr>\n <th>coin</th>\n <td>0.520579</td>\n <td>0.743240</td>\n <td>0.825229</td>\n <td>0.813151</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"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": "<Figure size 975.725x900 with 30 Axes>",
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},
"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": "<pyecharts.render.display.Javascript at 0x7fb1dd0400a0>",
"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": {
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},
"execution_count": 263,
"metadata": {},
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}
],
"source": [
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]
},
{
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"execution_count": 262,
"metadata": {
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"name": "#%%\n"
}
},
"outputs": [],
"source": []
}
],
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"language": "python",
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},
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},
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