diff --git a/.idea/Mybook.iml b/.idea/Mybook.iml
deleted file mode 100755
index 032c3de..0000000
--- a/.idea/Mybook.iml
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100755
index c23ecac..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,7 +0,0 @@
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diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100755
index ef11d87..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100755
index aee895c..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
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-</project>
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diff --git a/.idea/preferred-vcs.xml b/.idea/preferred-vcs.xml
deleted file mode 100644
index 848cfc4..0000000
--- a/.idea/preferred-vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100755
index 94a25f7..0000000
--- a/.idea/vcs.xml
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@@ -1,6 +0,0 @@
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diff --git a/README.md b/README.md
index cd87748..91883f4 100644
--- a/README.md
+++ b/README.md
@@ -1,50 +1,24 @@
-### 《Python数据分析入门————从数据获取到可视化》
+# 《Python数据分析入门——从数据获取到可视化》
 
-#### 概览
+<p align="center">
+<img src="./Asset/cover.jpeg" width="300" height="400" />
+</p>
 
-这里是本书中使用的所有源代码，数据等文件。关于本书的一些最新的进展的也会第一时间在这里公布。希望本书能对大家有所帮助。
+## News
+- Coming: 《写于出版6周年之后》
 
+## 概览
 
-#### 问题提交
+书籍[《Python数据分析入门——从数据获取到可视化》](http://www.broadview.com.cn/book/5010)
+中使用的所有源代码，数据等文件。
+关于本书的一些最新的进展的也会第一时间在这里公布。 
+希望本书能对大家有所帮助。
 
-如果大家有问题和建议，可以直接在本项目提交issue（推荐），也可以发邮件给我(datahonor@gmail.com)
-我会定期查看并尽快回复。
-(也有读者到[出版社](http://www.broadview.com.cn/book/5010)
-提交勘误的，也是可以的，不过只建议在那里提交typo相关的，
-涉及到代码还是建议在Github提issue，方便一些)。
 
+## 反馈建议
 
-#### 勘误
+- Issue/Discussion(推荐): 对于代码的问题可以提交Issue，对于其他问题可以在Discussion中讨论。
+- Email: 也可以发邮件给我(datahonor@gmail.com)，我会定期查看并尽快回复。
 
-已更正：
-
-| 页码 | 错误 | 改正 |
-|--------|--------|--------|
-|  201     |  上方第一个阴影框（训练集数据）“种类”列最后两行将“bumpy”全改为“orange”  | 第二次印刷时更正|
-|  202      |    第三行，“是橙子还是水果”改为“是橙子还是苹果”    |第二次印刷时更正|
-|  99     | 代码框最后两行交换位置（因为多线程会把`urls`清空）| 第六次印刷时更正 |
-|  115     |正文第三行“运行输出如下。”下面的输出有误，下面的数据需要我们自己手动创建 | 第六次印刷时更正 |
-|  245     | 代码框，最上面应加上`import random as rnd`| 第六次印刷时更正 |
-| 247,248 | 两个LP问题的目标函数漏掉，改正参考[博客](http://datahonor.com/2017/03/22/%E5%88%A9%E7%94%A8Python%E8%A7%A3%E7%BA%BF%E6%80%A7%E8%A7%84%E5%88%92%E9%97%AE%E9%A2%98-LP/)。 | 第六次印刷时更正 |
-| 71-73 | 豆瓣模拟登录报错 | 第六次印刷时更正 |
-
-
-
-待更正：
-
-| 页码 | 错误 | 改正 |
-|--------|--------|--------|
-
-
-
-
-
-#### 意见征集
-
-个人认为，一本书在出版后绝对不是结束的标志，而是新一轮的开始。本书写作的初衷在于，当时国内很多的书并没有将数据爬取，数据处理，分析以及可视化放到一起来写，我认为这是一件值得去尝试的事情，所以才有了这本书。
-
-在本书出版一年多来，根据各方的反馈也在不断进行着完善。于此同时也意识到书中存在的问题，比较核心的就在于知识的深度与广度之间的矛盾，本书是着眼于广度的，所以深度就有所欠缺。后面会考虑对内容进行删减，在顾及广度的同时突出重点（统计学方法，机器学习方法等算法）。
-
-此外，如果有机会写第二版，会将文章核心内容以Jupyter notebook的形式呈现，以更好地说明问题。
-
-如上所言，是有一些反馈，但是不太多。希望各位作为读者,在阅读完本书后能够写一些建议给我，我也能更好地明确下面修改的方向。
+## 勘误
+详见[勘误表](./errata.md)。
diff --git a/Report/.idea/.name b/Report/.idea/.name
deleted file mode 100644
index ddf3b8c..0000000
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deleted file mode 100644
index 89413d5..0000000
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-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
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-    <SUITE FILE_PATH="coverage/Report$get_data.coverage" NAME="get_data Coverage Results" MODIFIED="1523715027807" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/Source" />
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diff --git a/Report/Source/.ipynb_checkpoints/analy_vis_data-checkpoint.ipynb b/Report/Source/.ipynb_checkpoints/analy_vis_data-checkpoint.ipynb
deleted file mode 100644
index 613e126..0000000
--- a/Report/Source/.ipynb_checkpoints/analy_vis_data-checkpoint.ipynb
+++ /dev/null
@@ -1,376 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jieba\n",
-    "import pandas as pd\n",
-    "from snownlp import SnowNLP\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from wordcloud import WordCloud, STOPWORDS\n",
-    "from scipy.misc import imread"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c_date_time</th>\n",
-       "      <th>p_name</th>\n",
-       "      <th>p_url</th>\n",
-       "      <th>c_data</th>\n",
-       "      <th>c_rank</th>\n",
-       "      <th>c_recom</th>\n",
-       "      <th>c_date</th>\n",
-       "      <th>c_time</th>\n",
-       "      <th>c_rank_num</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2018-01-19 18:17:25</td>\n",
-       "      <td>王大根</td>\n",
-       "      <td>https://www.douban.com/people/diewithme/</td>\n",
-       "      <td>在这种家庭里做一条狗都好啊\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>6463</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>18:17:25</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2017-11-25 02:12:27</td>\n",
-       "      <td>李阿斗</td>\n",
-       "      <td>https://www.douban.com/people/gailsylee/</td>\n",
-       "      <td>当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3429</td>\n",
-       "      <td>2017-11-25</td>\n",
-       "      <td>02:12:27</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2017-12-06 15:10:45</td>\n",
-       "      <td>光明小卖部</td>\n",
-       "      <td>https://www.douban.com/people/gooooooooooohe/</td>\n",
-       "      <td>所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3349</td>\n",
-       "      <td>2017-12-06</td>\n",
-       "      <td>15:10:45</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2017-11-24 15:57:52</td>\n",
-       "      <td>同志亦凡人中文站</td>\n",
-       "      <td>https://www.douban.com/people/3540441/</td>\n",
-       "      <td>有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1814</td>\n",
-       "      <td>2017-11-24</td>\n",
-       "      <td>15:57:52</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2018-01-19 14:18:28</td>\n",
-       "      <td>桃桃淘电影</td>\n",
-       "      <td>https://www.douban.com/people/qijiuzhiyue/</td>\n",
-       "      <td>其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1711</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>14:18:28</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           c_date_time    p_name  \\\n",
-       "0  2018-01-19 18:17:25       王大根   \n",
-       "1  2017-11-25 02:12:27       李阿斗   \n",
-       "2  2017-12-06 15:10:45     光明小卖部   \n",
-       "3  2017-11-24 15:57:52  同志亦凡人中文站   \n",
-       "4  2018-01-19 14:18:28     桃桃淘电影   \n",
-       "\n",
-       "                                           p_url  \\\n",
-       "0       https://www.douban.com/people/diewithme/   \n",
-       "1       https://www.douban.com/people/gailsylee/   \n",
-       "2  https://www.douban.com/people/gooooooooooohe/   \n",
-       "3         https://www.douban.com/people/3540441/   \n",
-       "4     https://www.douban.com/people/qijiuzhiyue/   \n",
-       "\n",
-       "                                              c_data c_rank  c_recom  \\\n",
-       "0                            在这种家庭里做一条狗都好啊\\n             力荐     6463   \n",
-       "1   当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...     力荐     3429   \n",
-       "2            所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n             力荐     3349   \n",
-       "3   有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...     推荐     1814   \n",
-       "4   其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...     推荐     1711   \n",
-       "\n",
-       "       c_date    c_time  c_rank_num  \n",
-       "0  2018-01-19  18:17:25           5  \n",
-       "1  2017-11-25  02:12:27           5  \n",
-       "2  2017-12-06  15:10:45           5  \n",
-       "3  2017-11-24  15:57:52           4  \n",
-       "4  2018-01-19  14:18:28           4  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 读入处理好的数据\n",
-    "df = pd.read_csv('../data/douban_processed.csv')\n",
-    "\n",
-    "# 预览数据\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f29c03ede80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 分析评论文本\n",
-    "\n",
-    "# 情感分析\n",
-    "def get_sentiments(origin_s):\n",
-    "    s = SnowNLP(origin_s)\n",
-    "    return s.sentiments\n",
-    "\n",
-    "\n",
-    "df['c_sentiments'] = df['c_data'].apply(get_sentiments)\n",
-    "df['c_sentiments'].plot.hist()\n",
-    "plt.savefig('../data/positive.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['不',\n",
-       " '人',\n",
-       " '都',\n",
-       " '很',\n",
-       " '一个',\n",
-       " '电影',\n",
-       " '小',\n",
-       " '好',\n",
-       " '看',\n",
-       " '故事',\n",
-       " '里',\n",
-       " '会',\n",
-       " '太',\n",
-       " '中',\n",
-       " '男孩',\n",
-       " '孩子',\n",
-       " '还',\n",
-       " '世界',\n",
-       " '善良',\n",
-       " '家庭',\n",
-       " '最',\n",
-       " '说',\n",
-       " '更',\n",
-       " '去',\n",
-       " '上',\n",
-       " '姐姐',\n",
-       " '没',\n",
-       " '成长',\n",
-       " '视角',\n",
-       " '朋友']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 全部评论的关键字\n",
-    "all_comments = ''.join(df['c_data'])\n",
-    "all_snow = SnowNLP(all_comments)\n",
-    "keywords = all_snow.keywords(30)\n",
-    "keywords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['故事里每个人的结局都很好', '大家都不是一个人', '每个人都有故事每个人都有视角每个人都有选择']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 摘要\n",
-    "summary = all_snow.summary(3)\n",
-    "summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 其他应用机器学习进行探索..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f29bcfdbb00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 简单的可视化\n",
-    "sns.countplot('c_rank_num', data=df)\n",
-    "plt.savefig('../data/rank.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 1.011 seconds.\n",
-      "Prefix dict has been built succesfully.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f29b93f4f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 词云\n",
-    "def get_wordCloud(mylist):\n",
-    "    word_list = [\" \".join(jieba.cut(sentence)) for sentence in mylist]\n",
-    "    new_text = ' '.join(word_list)\n",
-    "    pic_path = '../data/heart.png'\n",
-    "    img_mask = imread(pic_path)\n",
-    "\n",
-    "    stopwords = set(STOPWORDS)\n",
-    "    stopwords.add(\"电影\")\n",
-    "    wordcloud = WordCloud(background_color=\"white\", max_words=2000, font_path='/home/shensir/Downloads/msyh.ttc',\n",
-    "                          mask=img_mask, stopwords=stopwords).generate(new_text)\n",
-    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
-    "    plt.axis(\"off\")\n",
-    "    plt.savefig('../data/wordcloud.png')\n",
-    "    \n",
-    "\n",
-    "get_wordCloud(df['c_data'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Report/Source/.ipynb_checkpoints/data_process-checkpoint.ipynb b/Report/Source/.ipynb_checkpoints/data_process-checkpoint.ipynb
deleted file mode 100644
index f7bbfb8..0000000
--- a/Report/Source/.ipynb_checkpoints/data_process-checkpoint.ipynb
+++ /dev/null
@@ -1,575 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 读入数据，给定各字段的名字\n",
-    "df = pd.read_csv('../data/douban.csv', header=None,\n",
-    "                 names=['p_name', 'p_url', 'c_date_time', 'c_data', 'c_rank', 'c_recom'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>p_name</th>\n",
-       "      <th>p_url</th>\n",
-       "      <th>c_date_time</th>\n",
-       "      <th>c_data</th>\n",
-       "      <th>c_rank</th>\n",
-       "      <th>c_recom</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>王大根</td>\n",
-       "      <td>https://www.douban.com/people/diewithme/</td>\n",
-       "      <td>2018-01-19 18:17:25</td>\n",
-       "      <td>在这种家庭里做一条狗都好啊\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>6463</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>李阿斗</td>\n",
-       "      <td>https://www.douban.com/people/gailsylee/</td>\n",
-       "      <td>2017-11-25 02:12:27</td>\n",
-       "      <td>当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>光明小卖部</td>\n",
-       "      <td>https://www.douban.com/people/gooooooooooohe/</td>\n",
-       "      <td>2017-12-06 15:10:45</td>\n",
-       "      <td>所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>同志亦凡人中文站</td>\n",
-       "      <td>https://www.douban.com/people/3540441/</td>\n",
-       "      <td>2017-11-24 15:57:52</td>\n",
-       "      <td>有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1814</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>桃桃淘电影</td>\n",
-       "      <td>https://www.douban.com/people/qijiuzhiyue/</td>\n",
-       "      <td>2018-01-19 14:18:28</td>\n",
-       "      <td>其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1711</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     p_name                                          p_url  \\\n",
-       "0       王大根       https://www.douban.com/people/diewithme/   \n",
-       "1       李阿斗       https://www.douban.com/people/gailsylee/   \n",
-       "2     光明小卖部  https://www.douban.com/people/gooooooooooohe/   \n",
-       "3  同志亦凡人中文站         https://www.douban.com/people/3540441/   \n",
-       "4     桃桃淘电影     https://www.douban.com/people/qijiuzhiyue/   \n",
-       "\n",
-       "           c_date_time                                             c_data  \\\n",
-       "0  2018-01-19 18:17:25                            在这种家庭里做一条狗都好啊\\n           \n",
-       "1  2017-11-25 02:12:27   当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...   \n",
-       "2  2017-12-06 15:10:45            所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n           \n",
-       "3  2017-11-24 15:57:52   有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...   \n",
-       "4  2018-01-19 14:18:28   其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...   \n",
-       "\n",
-       "  c_rank  c_recom  \n",
-       "0     力荐     6463  \n",
-       "1     力荐     3429  \n",
-       "2     力荐     3349  \n",
-       "3     推荐     1814  \n",
-       "4     推荐     1711  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 预览数据\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "p_name         0\n",
-      "p_url          0\n",
-      "c_date_time    0\n",
-      "c_data         0\n",
-      "c_rank         0\n",
-      "c_recom        0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 缺失值检测与去除\n",
-    "print(df.isnull().sum())\n",
-    "#df.dropna(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 拆分原c_date_time为c_date和c_time\n",
-    "def get_date(date_time):\n",
-    "    # 有时会格式不对\n",
-    "    if len(date_time) < 10:\n",
-    "        return None\n",
-    "    return re.findall(r'(\\d+-\\d+-\\d+) \\d+.*?', date_time)[0]\n",
-    "\n",
-    "\n",
-    "def get_time(date_time):\n",
-    "    if len(date_time) < 10:\n",
-    "        return None\n",
-    "    return re.findall(r'.*? (\\d+:\\d+:\\d+)', date_time)[0]\n",
-    "\n",
-    "\n",
-    "df['c_date'] = df['c_date_time'].apply(get_date)\n",
-    "df['c_time'] = df['c_date_time'].apply(get_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>p_name</th>\n",
-       "      <th>p_url</th>\n",
-       "      <th>c_date_time</th>\n",
-       "      <th>c_data</th>\n",
-       "      <th>c_rank</th>\n",
-       "      <th>c_recom</th>\n",
-       "      <th>c_date</th>\n",
-       "      <th>c_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>王大根</td>\n",
-       "      <td>https://www.douban.com/people/diewithme/</td>\n",
-       "      <td>2018-01-19 18:17:25</td>\n",
-       "      <td>在这种家庭里做一条狗都好啊\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>6463</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>18:17:25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>李阿斗</td>\n",
-       "      <td>https://www.douban.com/people/gailsylee/</td>\n",
-       "      <td>2017-11-25 02:12:27</td>\n",
-       "      <td>当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3429</td>\n",
-       "      <td>2017-11-25</td>\n",
-       "      <td>02:12:27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>光明小卖部</td>\n",
-       "      <td>https://www.douban.com/people/gooooooooooohe/</td>\n",
-       "      <td>2017-12-06 15:10:45</td>\n",
-       "      <td>所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3349</td>\n",
-       "      <td>2017-12-06</td>\n",
-       "      <td>15:10:45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>同志亦凡人中文站</td>\n",
-       "      <td>https://www.douban.com/people/3540441/</td>\n",
-       "      <td>2017-11-24 15:57:52</td>\n",
-       "      <td>有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1814</td>\n",
-       "      <td>2017-11-24</td>\n",
-       "      <td>15:57:52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>桃桃淘电影</td>\n",
-       "      <td>https://www.douban.com/people/qijiuzhiyue/</td>\n",
-       "      <td>2018-01-19 14:18:28</td>\n",
-       "      <td>其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1711</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>14:18:28</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     p_name                                          p_url  \\\n",
-       "0       王大根       https://www.douban.com/people/diewithme/   \n",
-       "1       李阿斗       https://www.douban.com/people/gailsylee/   \n",
-       "2     光明小卖部  https://www.douban.com/people/gooooooooooohe/   \n",
-       "3  同志亦凡人中文站         https://www.douban.com/people/3540441/   \n",
-       "4     桃桃淘电影     https://www.douban.com/people/qijiuzhiyue/   \n",
-       "\n",
-       "           c_date_time                                             c_data  \\\n",
-       "0  2018-01-19 18:17:25                            在这种家庭里做一条狗都好啊\\n           \n",
-       "1  2017-11-25 02:12:27   当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...   \n",
-       "2  2017-12-06 15:10:45            所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n           \n",
-       "3  2017-11-24 15:57:52   有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...   \n",
-       "4  2018-01-19 14:18:28   其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...   \n",
-       "\n",
-       "  c_rank  c_recom      c_date    c_time  \n",
-       "0     力荐     6463  2018-01-19  18:17:25  \n",
-       "1     力荐     3429  2017-11-25  02:12:27  \n",
-       "2     力荐     3349  2017-12-06  15:10:45  \n",
-       "3     推荐     1814  2017-11-24  15:57:52  \n",
-       "4     推荐     1711  2018-01-19  14:18:28  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 预览数据\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before->\n",
-      " p_name         object\n",
-      "p_url          object\n",
-      "c_date_time    object\n",
-      "c_data         object\n",
-      "c_rank         object\n",
-      "c_recom         int64\n",
-      "c_date         object\n",
-      "c_time         object\n",
-      "dtype: object\n",
-      "After->\n",
-      " p_name                 object\n",
-      "p_url                  object\n",
-      "c_date_time    datetime64[ns]\n",
-      "c_data                 object\n",
-      "c_rank                 object\n",
-      "c_recom                 int64\n",
-      "c_date                 object\n",
-      "c_time                 object\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 如果需要，也可以进行数据类型的转换\n",
-    "print('Before->\\n', df.dtypes)\n",
-    "df['c_date_time'] = df['c_date_time'].astype('datetime64[ns]')\n",
-    "print('After->\\n',  df.dtypes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 也可方便地进行数据转换[Encoding Categorical Values]\n",
-    "# 将汉字对应编码为数字\n",
-    "def trans(words):\n",
-    "    if words == '力荐':\n",
-    "        return 5\n",
-    "    elif words == '推荐':\n",
-    "        return 4\n",
-    "    elif words == '还行':\n",
-    "        return 3\n",
-    "    elif words == '较差':\n",
-    "        return 2\n",
-    "    elif words == '很差':\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "df['c_rank_num'] = df['c_rank'].apply(trans)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>p_name</th>\n",
-       "      <th>p_url</th>\n",
-       "      <th>c_date_time</th>\n",
-       "      <th>c_data</th>\n",
-       "      <th>c_rank</th>\n",
-       "      <th>c_recom</th>\n",
-       "      <th>c_date</th>\n",
-       "      <th>c_time</th>\n",
-       "      <th>c_rank_num</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>王大根</td>\n",
-       "      <td>https://www.douban.com/people/diewithme/</td>\n",
-       "      <td>2018-01-19 18:17:25</td>\n",
-       "      <td>在这种家庭里做一条狗都好啊\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>6463</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>18:17:25</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>李阿斗</td>\n",
-       "      <td>https://www.douban.com/people/gailsylee/</td>\n",
-       "      <td>2017-11-25 02:12:27</td>\n",
-       "      <td>当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3429</td>\n",
-       "      <td>2017-11-25</td>\n",
-       "      <td>02:12:27</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>光明小卖部</td>\n",
-       "      <td>https://www.douban.com/people/gooooooooooohe/</td>\n",
-       "      <td>2017-12-06 15:10:45</td>\n",
-       "      <td>所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n</td>\n",
-       "      <td>力荐</td>\n",
-       "      <td>3349</td>\n",
-       "      <td>2017-12-06</td>\n",
-       "      <td>15:10:45</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>同志亦凡人中文站</td>\n",
-       "      <td>https://www.douban.com/people/3540441/</td>\n",
-       "      <td>2017-11-24 15:57:52</td>\n",
-       "      <td>有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1814</td>\n",
-       "      <td>2017-11-24</td>\n",
-       "      <td>15:57:52</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>桃桃淘电影</td>\n",
-       "      <td>https://www.douban.com/people/qijiuzhiyue/</td>\n",
-       "      <td>2018-01-19 14:18:28</td>\n",
-       "      <td>其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...</td>\n",
-       "      <td>推荐</td>\n",
-       "      <td>1711</td>\n",
-       "      <td>2018-01-19</td>\n",
-       "      <td>14:18:28</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     p_name                                          p_url  \\\n",
-       "0       王大根       https://www.douban.com/people/diewithme/   \n",
-       "1       李阿斗       https://www.douban.com/people/gailsylee/   \n",
-       "2     光明小卖部  https://www.douban.com/people/gooooooooooohe/   \n",
-       "3  同志亦凡人中文站         https://www.douban.com/people/3540441/   \n",
-       "4     桃桃淘电影     https://www.douban.com/people/qijiuzhiyue/   \n",
-       "\n",
-       "          c_date_time                                             c_data  \\\n",
-       "0 2018-01-19 18:17:25                            在这种家庭里做一条狗都好啊\\n           \n",
-       "1 2017-11-25 02:12:27   当时出国以后的第一最大感受就是尊重，不论老弱病孕还是任何“与众不同”，都不会有人上下打量你...   \n",
-       "2 2017-12-06 15:10:45            所有人都知道是化妆只有我一个人以为请的真实病人出演吗。。。\\n           \n",
-       "3 2017-11-24 15:57:52   有种糖放多了的感觉，精华基本都在预告里了。但对孩子们的纯真友情毫无抵抗力啊，就像被温柔的抚...   \n",
-       "4 2018-01-19 14:18:28   其实这更像当代童话，因为，实在是太暖了。里面每个人都那么暖，怎么可以那么暖，怎么可以那么暖...   \n",
-       "\n",
-       "  c_rank  c_recom      c_date    c_time  c_rank_num  \n",
-       "0     力荐     6463  2018-01-19  18:17:25           5  \n",
-       "1     力荐     3429  2017-11-25  02:12:27           5  \n",
-       "2     力荐     3349  2017-12-06  15:10:45           5  \n",
-       "3     推荐     1814  2017-11-24  15:57:52           4  \n",
-       "4     推荐     1711  2018-01-19  14:18:28           4  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 预览数据\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 设置索引列为c_date_time\n",
-    "df.index = df['c_date_time']\n",
-    "\n",
-    "# 去除多余的c_date_time列\n",
-    "df = df.drop(['c_date_time'], axis=1)\n",
-    "\n",
-    "# 其他的一些操作..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 去除操作产生的缺失值\n",
-    "df.dropna(inplace=True)\n",
-    "# 保存预处理后的文件\n",
-    "df.to_csv('douban_processed.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Report/venv/bin/activate b/Report/venv/bin/activate
deleted file mode 100644
index 41fef6b..0000000
--- a/Report/venv/bin/activate
+++ /dev/null
@@ -1,76 +0,0 @@
-# This file must be used with "source bin/activate" *from bash*
-# you cannot run it directly
-
-deactivate () {
-    # reset old environment variables
-    if [ -n "$_OLD_VIRTUAL_PATH" ] ; then
-        PATH="$_OLD_VIRTUAL_PATH"
-        export PATH
-        unset _OLD_VIRTUAL_PATH
-    fi
-    if [ -n "$_OLD_VIRTUAL_PYTHONHOME" ] ; then
-        PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
-        export PYTHONHOME
-        unset _OLD_VIRTUAL_PYTHONHOME
-    fi
-
-    # This should detect bash and zsh, which have a hash command that must
-    # be called to get it to forget past commands.  Without forgetting
-    # past commands the $PATH changes we made may not be respected
-    if [ -n "$BASH" -o -n "$ZSH_VERSION" ] ; then
-        hash -r
-    fi
-
-    if [ -n "$_OLD_VIRTUAL_PS1" ] ; then
-        PS1="$_OLD_VIRTUAL_PS1"
-        export PS1
-        unset _OLD_VIRTUAL_PS1
-    fi
-
-    unset VIRTUAL_ENV
-    if [ ! "$1" = "nondestructive" ] ; then
-    # Self destruct!
-        unset -f deactivate
-    fi
-}
-
-# unset irrelevant variables
-deactivate nondestructive
-
-VIRTUAL_ENV="/home/shensir/Documents/MyPrograming/Python/PycharmProject/Repot/venv"
-export VIRTUAL_ENV
-
-_OLD_VIRTUAL_PATH="$PATH"
-PATH="$VIRTUAL_ENV/bin:$PATH"
-export PATH
-
-# unset PYTHONHOME if set
-# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
-# could use `if (set -u; : $PYTHONHOME) ;` in bash
-if [ -n "$PYTHONHOME" ] ; then
-    _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
-    unset PYTHONHOME
-fi
-
-if [ -z "$VIRTUAL_ENV_DISABLE_PROMPT" ] ; then
-    _OLD_VIRTUAL_PS1="$PS1"
-    if [ "x(venv) " != x ] ; then
-	PS1="(venv) $PS1"
-    else
-    if [ "`basename \"$VIRTUAL_ENV\"`" = "__" ] ; then
-        # special case for Aspen magic directories
-        # see http://www.zetadev.com/software/aspen/
-        PS1="[`basename \`dirname \"$VIRTUAL_ENV\"\``] $PS1"
-    else
-        PS1="(`basename \"$VIRTUAL_ENV\"`)$PS1"
-    fi
-    fi
-    export PS1
-fi
-
-# This should detect bash and zsh, which have a hash command that must
-# be called to get it to forget past commands.  Without forgetting
-# past commands the $PATH changes we made may not be respected
-if [ -n "$BASH" -o -n "$ZSH_VERSION" ] ; then
-    hash -r
-fi
diff --git a/Report/venv/bin/activate.csh b/Report/venv/bin/activate.csh
deleted file mode 100644
index 09f999f..0000000
--- a/Report/venv/bin/activate.csh
+++ /dev/null
@@ -1,37 +0,0 @@
-# This file must be used with "source bin/activate.csh" *from csh*.
-# You cannot run it directly.
-# Created by Davide Di Blasi <davidedb@gmail.com>.
-# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
-
-alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate'
-
-# Unset irrelevant variables.
-deactivate nondestructive
-
-setenv VIRTUAL_ENV "/home/shensir/Documents/MyPrograming/Python/PycharmProject/Repot/venv"
-
-set _OLD_VIRTUAL_PATH="$PATH"
-setenv PATH "$VIRTUAL_ENV/bin:$PATH"
-
-
-set _OLD_VIRTUAL_PROMPT="$prompt"
-
-if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
-    if ("venv" != "") then
-        set env_name = "venv"
-    else
-        if (`basename "VIRTUAL_ENV"` == "__") then
-            # special case for Aspen magic directories
-            # see http://www.zetadev.com/software/aspen/
-            set env_name = `basename \`dirname "$VIRTUAL_ENV"\``
-        else
-            set env_name = `basename "$VIRTUAL_ENV"`
-        endif
-    endif
-    set prompt = "[$env_name] $prompt"
-    unset env_name
-endif
-
-alias pydoc python -m pydoc
-
-rehash
diff --git a/Report/venv/bin/activate.fish b/Report/venv/bin/activate.fish
deleted file mode 100644
index 35dae94..0000000
--- a/Report/venv/bin/activate.fish
+++ /dev/null
@@ -1,75 +0,0 @@
-# This file must be used with ". bin/activate.fish" *from fish* (http://fishshell.org)
-# you cannot run it directly
-
-function deactivate  -d "Exit virtualenv and return to normal shell environment"
-    # reset old environment variables
-    if test -n "$_OLD_VIRTUAL_PATH"
-        set -gx PATH $_OLD_VIRTUAL_PATH
-        set -e _OLD_VIRTUAL_PATH
-    end
-    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
-        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
-        set -e _OLD_VIRTUAL_PYTHONHOME
-    end
-
-    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
-        functions -e fish_prompt
-        set -e _OLD_FISH_PROMPT_OVERRIDE
-        functions -c _old_fish_prompt fish_prompt
-        functions -e _old_fish_prompt
-    end
-
-    set -e VIRTUAL_ENV
-    if test "$argv[1]" != "nondestructive"
-        # Self destruct!
-        functions -e deactivate
-    end
-end
-
-# unset irrelevant variables
-deactivate nondestructive
-
-set -gx VIRTUAL_ENV "/home/shensir/Documents/MyPrograming/Python/PycharmProject/Repot/venv"
-
-set -gx _OLD_VIRTUAL_PATH $PATH
-set -gx PATH "$VIRTUAL_ENV/bin" $PATH
-
-# unset PYTHONHOME if set
-if set -q PYTHONHOME
-    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
-    set -e PYTHONHOME
-end
-
-if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
-    # fish uses a function instead of an env var to generate the prompt.
-
-    # save the current fish_prompt function as the function _old_fish_prompt
-    functions -c fish_prompt _old_fish_prompt
-
-    # with the original prompt function renamed, we can override with our own.
-    function fish_prompt
-        # Save the return status of the last command
-        set -l old_status $status
-
-        # Prompt override?
-        if test -n "(venv) "            
-            printf "%s%s" "(venv) " (set_color normal)
-        else
-            # ...Otherwise, prepend env
-            set -l _checkbase (basename "$VIRTUAL_ENV")
-            if test $_checkbase = "__"
-                # special case for Aspen magic directories
-                # see http://www.zetadev.com/software/aspen/
-                printf "%s[%s]%s " (set_color -b blue white) (basename (dirname "$VIRTUAL_ENV")) (set_color normal)
-            else
-                printf "%s(%s)%s" (set_color -b blue white) (basename "$VIRTUAL_ENV") (set_color normal)
-            end
-        end
-
-        # Restore the return status of the previous command.
-        echo "exit $old_status" | .
-        _old_fish_prompt
-    end
-
-    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
-end
diff --git a/Report/venv/bin/python b/Report/venv/bin/python
deleted file mode 100755
index e525e66..0000000
Binary files a/Report/venv/bin/python and /dev/null differ
diff --git a/Report/venv/bin/python3 b/Report/venv/bin/python3
deleted file mode 100755
index e525e66..0000000
Binary files a/Report/venv/bin/python3 and /dev/null differ
diff --git a/Report/venv/lib64 b/Report/venv/lib64
deleted file mode 120000
index 7951405..0000000
--- a/Report/venv/lib64
+++ /dev/null
@@ -1 +0,0 @@
-lib
\ No newline at end of file
diff --git a/Report/venv/pyvenv.cfg b/Report/venv/pyvenv.cfg
deleted file mode 100644
index 2ec7b1e..0000000
--- a/Report/venv/pyvenv.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-home = /home/shensir/anaconda3/bin
-include-system-site-packages = false
-version = 3.6.1
diff --git a/errata.md b/errata.md
new file mode 100644
index 0000000..a1b9d33
--- /dev/null
+++ b/errata.md
@@ -0,0 +1,11 @@
+# 勘误
+
+| 页码      | 错误                                          | 改正       |
+|---------|---------------------------------------------|----------|
+| 201     | 上方第一个阴影框（训练集数据）“种类”列最后两行将“bumpy”全改为“orange” | 第二次印刷时更正 |
+| 202     | 第三行，“是橙子还是水果”改为“是橙子还是苹果”                    | 第二次印刷时更正 |
+| 99      | 代码框最后两行交换位置（因为多线程会把`urls`清空）                | 第六次印刷时更正 |
+| 115     | 正文第三行“运行输出如下。”下面的输出有误，下面的数据需要我们自己手动创建       | 第六次印刷时更正 |
+| 245     | 代码框，最上面应加上`import random as rnd`            | 第六次印刷时更正 |
+| 247,248 | 两个LP问题的目标函数漏掉                               | 第六次印刷时更正 |
+| 71-73   | 豆瓣模拟登录报错                                    | 第六次印刷时更正 |