GFW Visualization - Public - pt3 - Interactive charts.ipynb

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      "C:\\Users\\Wayne\\Dropbox\\Acer Laptop Sync\\Data Science\\GFW Visualization\n",
      "('Total # of tweets in SQLite:', 40283)\n",
      "('Total # of tweets in filtered PANDAS dataframe:', 22326)\n"
     ]
    }
   ],
   "source": [
    "# set working directory \n",
    "get_ipython().magic(u'pwd')\n",
    "get_ipython().magic(u'cd \"C:/Users/Wayne/Dropbox/Acer Laptop Sync/Data Science/GFW Visualization\"')\n",
    "\n",
    "import sys\n",
    "import json\n",
    "import sqlite3\n",
    "import time\n",
    "import datetime\n",
    "from pprint import pprint\n",
    "import string\n",
    "import itertools\n",
    "\n",
    "import unicodedata   \n",
    "\n",
    "from numpy import random, asarray, linspace, corrcoef\n",
    "import pandas as pd\n",
    "from pandas import Series\n",
    "\n",
    "import jupyter_client\n",
    "import ipykernel\n",
    "\n",
    "from lightning import Lightning\n",
    "\n",
    "import sqlalchemy\n",
    "from sqlalchemy.orm import mapper, sessionmaker\n",
    "from sqlalchemy import Column, Integer, String, ForeignKey, Text, DateTime, Unicode\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from types import *\n",
    "from sqlalchemy import func     #TO PERFORM 'COUNT' QUERIES\n",
    "from sqlalchemy import distinct #TO COUNT 'DISTINCT' VALUES IN DB\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import* \n",
    "\n",
    "from table_classes_users_followers_v2 import * # make sure this .py file exists in your working directory\n",
    "\n",
    "# connect to an existing database\n",
    "engine = sqlalchemy.create_engine(\"sqlite:///C:/Users/Wayne/Dropbox/Acer Laptop Sync/Data Science/GFW Visualization/GFW_tweets starting 9-1-2015.sqlite\", echo=False)   #ABSOLUTE PATH TO DB       \n",
    "Session = sessionmaker(bind=engine)\n",
    "session = Session()  \n",
    "Base.metadata.create_all(engine)\n",
    "all_tweets = session.query(TWEET).all()\n",
    "\n",
    "con = sqlite3.connect(\"C:/Users/Wayne/Dropbox/Acer Laptop Sync/Data Science/GFW Visualization/GFW_tweets starting 9-1-2015.sqlite\")\n",
    "cur = con.cursor()\n",
    "df = pd.read_sql_query(\"SELECT * from gfw_tweets\", con)\n",
    "con.close()\n",
    "\n",
    "df['created_at'] = pd.to_datetime(df['created_at'])\n",
    "df['created_at'] = df['created_at'].apply(lambda x: x.date())\n",
    "df = df.loc[df['query'].isin(['%23FuckGFW','%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91','%E7%BF%BB%E5%A2%99','%E9%98%B2%E7%81%AB%E5%A2%99','greatfirewall'])]\n",
    "\n",
    "print (\"Total # of tweets in SQLite:\", len(all_tweets))\n",
    "print (\"Total # of tweets in filtered PANDAS dataframe:\", len(df))\n",
    "\n",
    "df = df.sort(['created_at'])\n",
    "df = df.set_index(['created_at'])\n",
    "\n",
    "def fuckgfwcumsum(row):\n",
    "    if row['query'] == \"%23FuckGFW\":\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "def kxswcumsum(row):\n",
    "    if row['query'] == \"%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91\":\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "def fqcumsum(row):\n",
    "    if row['query'] == \"%E7%BF%BB%E5%A2%99\":\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "def fhqcumsum(row):\n",
    "    if row['query'] == \"%E9%98%B2%E7%81%AB%E5%A2%99\":\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "def greatfirewallcumsum(row):\n",
    "    if row['query'] == \"greatfirewall\":\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "df['fuckgfw_base_tweet_count'] = df.apply(lambda row: fuckgfwcumsum(row), axis=1)    \n",
    "df['fuckgfw_cum_tweet_count'] = df.fuckgfw_base_tweet_count.cumsum()\n",
    "df['kxsw_base_tweet_count'] = df.apply(lambda row: kxswcumsum(row), axis=1)    \n",
    "df['kxsw_cum_tweet_count'] = df.kxsw_base_tweet_count.cumsum()\n",
    "df['fq_base_tweet_count'] = df.apply(lambda row: fqcumsum(row), axis=1)    \n",
    "df['fq_cum_tweet_count'] = df.fq_base_tweet_count.cumsum()\n",
    "df['fhq_base_tweet_count'] = df.apply(lambda row: fhqcumsum(row), axis=1)    \n",
    "df['fhq_cum_tweet_count'] = df.fhq_base_tweet_count.cumsum()\n",
    "df['greatfirewall_base_tweet_count'] = df.apply(lambda row: greatfirewallcumsum(row), axis=1)    \n",
    "df['greatfirewall_cum_tweet_count'] = df.greatfirewall_base_tweet_count.cumsum()\n",
    "\n",
    "def dailycount(x):\n",
    "            return Series(dict(Number_of_tweets = x['content'].count(),\n",
    "                        ))\n",
    "\n",
    "fuckgfw_daily_count = df[df['query'] == '%23FuckGFW'].groupby(df[df['query'] == '%23FuckGFW'].index).apply(dailycount)\n",
    "kxsw_daily_count = df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].groupby(df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].index).apply(dailycount)\n",
    "fq_daily_count = df[df['query'] == '%E7%BF%BB%E5%A2%99'].groupby(df[df['query'] == '%E7%BF%BB%E5%A2%99'].index).apply(dailycount)\n",
    "fhq_daily_count = df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].groupby(df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].index).apply(dailycount)\n",
    "greatfirewall_daily_count = df[df['query'] == 'greatfirewall'].groupby(df[df['query'] == 'greatfirewall'].index).apply(dailycount)\n",
    "\n",
    "fuckgfw_daily_count['fuckgfw_count'] =  fuckgfw_daily_count['Number_of_tweets']\n",
    "kxsw_daily_count['kxsw_count'] = kxsw_daily_count['Number_of_tweets']\n",
    "fq_daily_count['fq_count'] = fq_daily_count['Number_of_tweets']\n",
    "fhq_daily_count['fhq_count'] = fhq_daily_count['Number_of_tweets']\n",
    "greatfirewall_daily_count['greatfirewall_count'] = greatfirewall_daily_count['Number_of_tweets']\n",
    "\n",
    "dailycount_forall = pd.concat([fuckgfw_daily_count, kxsw_daily_count, fq_daily_count, fhq_daily_count,greatfirewall_daily_count], axis=1)\n",
    "dailycount_forall = dailycount_forall[['fuckgfw_count', 'kxsw_count','fq_count','fhq_count','greatfirewall_count']]\n",
    "dailycount_forall = dailycount_forall.fillna(0)\n",
    "\n",
    "def dailyrtcount(x):\n",
    "    return Series(dict(Number_of_rts = x['retweet_count'].sum(),\n",
    "                        ))\n",
    "\n",
    "def fuckgfwvirality(x):\n",
    "    return x['fuckgfw_rtcount']/x['fuckgfw_count']\n",
    "\n",
    "def kxswvirality(x):\n",
    "    return x['kxsw_rtcount']/x['kxsw_count']\n",
    "\n",
    "def fqvirality(x):\n",
    "    return x['fq_rtcount']/x['fq_count']\n",
    "\n",
    "def fhqvirality(x):\n",
    "    return x['fhq_rtcount']/x['fhq_count']\n",
    "\n",
    "def greatfirewallvirality(x):\n",
    "    return x['greatfirewall_rtcount']/x['greatfirewall_count']\n",
    "\n",
    "fuckgfw_rt_count = df[df['query'] == '%23FuckGFW'].groupby(df[df['query'] == '%23FuckGFW'].index).apply(dailyrtcount)\n",
    "kxsw_rt_count = df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].groupby(df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].index).apply(dailyrtcount)\n",
    "fq_rt_count = df[df['query'] == '%E7%BF%BB%E5%A2%99'].groupby(df[df['query'] == '%E7%BF%BB%E5%A2%99'].index).apply(dailyrtcount)\n",
    "fhq_rt_count = df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].groupby(df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].index).apply(dailyrtcount)\n",
    "greatfirewall_rt_count = df[df['query'] == 'greatfirewall'].groupby(df[df['query'] == 'greatfirewall'].index).apply(dailyrtcount)\n",
    "\n",
    "fuckgfw_rt_count['fuckgfw_rtcount'] =  fuckgfw_rt_count['Number_of_rts']\n",
    "kxsw_rt_count['kxsw_rtcount'] = kxsw_rt_count['Number_of_rts']\n",
    "fq_rt_count['fq_rtcount'] = fq_rt_count['Number_of_rts']\n",
    "fhq_rt_count['fhq_rtcount'] = fhq_rt_count['Number_of_rts']\n",
    "greatfirewall_rt_count['greatfirewall_rtcount'] = greatfirewall_rt_count['Number_of_rts']\n",
    "\n",
    "#merge1 = pd.merge(fuckgfw_daily_count, kxsw_daily_count, how='inner', on= None, left_on=None, right_on=None,\n",
    "#      left_index=None, right_index=False, sort=True, suffixes=('_x', '_y'), copy=True)\n",
    "#print len(fuckgfw_daily_count)\n",
    "#merge1 \n",
    "rtcount_forall = pd.concat([fuckgfw_rt_count, kxsw_rt_count, fq_rt_count, fhq_rt_count,greatfirewall_rt_count ], axis=1)\n",
    "rtcount_forall = rtcount_forall[['fuckgfw_rtcount', 'kxsw_rtcount','fq_rtcount','fhq_rtcount','greatfirewall_rtcount']]\n",
    "rtcount_forall = rtcount_forall.fillna(0)\n",
    "rtandcountcombined = pd.concat([rtcount_forall,dailycount_forall], axis=1)\n",
    "rtandcountcombined['fuckgfw_virality'] = rtandcountcombined.apply(lambda x:fuckgfwvirality(x), axis=1)\n",
    "rtandcountcombined['kxsq_virality'] = rtandcountcombined.apply(lambda x:kxswvirality(x), axis=1)\n",
    "rtandcountcombined['fq_virality'] = rtandcountcombined.apply(lambda x:fqvirality(x), axis=1)\n",
    "rtandcountcombined['fhq_virality'] = rtandcountcombined.apply(lambda x:fhqvirality(x), axis=1)\n",
    "rtandcountcombined['greatfirewall_virality'] = rtandcountcombined.apply(lambda x:greatfirewallvirality(x), axis=1)\n",
    "virality_display = rtandcountcombined[['fuckgfw_virality','kxsq_virality','fq_virality','fhq_virality','greatfirewall_virality']]\n",
    "\n",
    "def dailyfollowercount(x):\n",
    "    return Series(dict(Number_of_followers = x['from_user_followers_count'].sum(),\n",
    "                        ))\n",
    "\n",
    "fuckgfw_follower_count = df[df['query'] == '%23FuckGFW'].groupby(df[df['query'] == '%23FuckGFW'].index).apply(dailyfollowercount)\n",
    "kxsw_follower_count = df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].groupby(df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].index).apply(dailyfollowercount)\n",
    "fq_follower_count = df[df['query'] == '%E7%BF%BB%E5%A2%99'].groupby(df[df['query'] == '%E7%BF%BB%E5%A2%99'].index).apply(dailyfollowercount)\n",
    "fhq_follower_count = df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].groupby(df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].index).apply(dailyfollowercount)\n",
    "greatfirewall_follower_count = df[df['query'] == 'greatfirewall'].groupby(df[df['query'] == 'greatfirewall'].index).apply(dailyfollowercount)\n",
    "\n",
    "fuckgfw_follower_count['fuckgfw_followercount'] =  fuckgfw_follower_count['Number_of_followers']\n",
    "kxsw_follower_count['kxsw_followercount'] = kxsw_follower_count['Number_of_followers']\n",
    "fq_follower_count['fq_followercount'] = fq_follower_count['Number_of_followers']\n",
    "fhq_follower_count['fhq_followercount'] = fhq_follower_count['Number_of_followers']\n",
    "greatfirewall_follower_count['greatfirewall_followercount'] = greatfirewall_follower_count['Number_of_followers']\n",
    "\n",
    "followercount_forall = pd.concat([fuckgfw_follower_count, kxsw_follower_count, fq_follower_count, fhq_follower_count,greatfirewall_follower_count ], axis=1)\n",
    "followercount_forall = followercount_forall[['fuckgfw_followercount', 'kxsw_followercount','fq_followercount','fhq_followercount','greatfirewall_followercount']]\n",
    "followercount_forall = followercount_forall.fillna(0)\n",
    "\n",
    "def dailydiscussantscount(x):\n",
    "    return Series(dict(Number_of_discussants = len(pd.unique(x['from_user_screen_name'].ravel())),\n",
    "                        ))\n",
    "\n",
    "fuckgfw_discussant_count = df[df['query'] == '%23FuckGFW'].groupby(df[df['query'] == '%23FuckGFW'].index).apply(dailydiscussantscount)\n",
    "kxsw_discussant_count = df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].groupby(df[df['query'] == '%E7%A7%91%E5%AD%A6%E4%B8%8A%E7%BD%91'].index).apply(dailydiscussantscount)\n",
    "fq_discussant_count = df[df['query'] == '%E7%BF%BB%E5%A2%99'].groupby(df[df['query'] == '%E7%BF%BB%E5%A2%99'].index).apply(dailydiscussantscount)\n",
    "fhq_discussant_count = df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].groupby(df[df['query'] == '%E9%98%B2%E7%81%AB%E5%A2%99'].index).apply(dailydiscussantscount)\n",
    "greatfirewall_discussant_count = df[df['query'] == 'greatfirewall'].groupby(df[df['query'] == 'greatfirewall'].index).apply(dailydiscussantscount)\n",
    "\n",
    "fuckgfw_discussant_count['fuckgfw_discussantcount'] =  fuckgfw_discussant_count['Number_of_discussants']\n",
    "kxsw_discussant_count['kxsw_discussantcount'] = kxsw_discussant_count['Number_of_discussants']\n",
    "fq_discussant_count['fq_discussantcount'] = fq_discussant_count['Number_of_discussants']\n",
    "fhq_discussant_count['fhq_discussantcount'] = fhq_discussant_count['Number_of_discussants']\n",
    "greatfirewall_discussant_count['greatfirewall_discussantcount'] = greatfirewall_discussant_count['Number_of_discussants']\n",
    "\n",
    "discussant_forall = pd.concat([fuckgfw_discussant_count, kxsw_discussant_count, fq_discussant_count, fhq_discussant_count,greatfirewall_discussant_count ], axis=1)\n",
    "discussant_forall = discussant_forall[['fuckgfw_discussantcount', 'kxsw_discussantcount','fq_discussantcount','fhq_discussantcount','greatfirewall_discussantcount']]\n",
    "discussant_forall = discussant_forall.fillna(0)\n",
    "\n",
    "def fuckgfwaudiencesize(x):\n",
    "    return x['fuckgfw_followercount']/x['fuckgfw_discussantcount']\n",
    "\n",
    "def kxswaudiencesize(x):\n",
    "    return x['kxsw_followercount']/x['kxsw_discussantcount']\n",
    "\n",
    "def fqaudiencesize(x):\n",
    "    return x['fq_followercount']/x['fq_discussantcount']\n",
    "\n",
    "def fhqaudiencesize(x):\n",
    "    return x['fhq_followercount']/x['fhq_discussantcount']\n",
    "\n",
    "def greatfirewallaudiencesize(x):\n",
    "    return x['greatfirewall_followercount']/x['greatfirewall_discussantcount']\n",
    "\n",
    "audiencesizecombined = pd.concat([discussant_forall,followercount_forall], axis=1)\n",
    "audiencesizecombined['fuckgfw_audiencesize'] = audiencesizecombined.apply(lambda x: fuckgfwaudiencesize(x), axis=1)\n",
    "audiencesizecombined['kxsw_audiencesize'] = audiencesizecombined.apply(lambda x:kxswaudiencesize(x), axis=1)\n",
    "audiencesizecombined['fq_audiencesize'] = audiencesizecombined.apply(lambda x:fqaudiencesize(x), axis=1)\n",
    "audiencesizecombined['fhq_audiencesize'] = audiencesizecombined.apply(lambda x:fhqaudiencesize(x), axis=1)\n",
    "audiencesizecombined['greatfirewall_audiencesize'] = audiencesizecombined.apply(lambda x:greatfirewallaudiencesize(x), axis=1)\n",
    "audiencesize_display = audiencesizecombined[['fuckgfw_audiencesize','kxsw_audiencesize','fq_audiencesize','fhq_audiencesize','greatfirewall_audiencesize']]\n",
    "audiencesize_display = audiencesize_display.fillna(0)\n",
    "audiencesize_display['fuckgfw_audiencesize'] = audiencesize_display['fuckgfw_audiencesize'].astype(int)\n",
    "audiencesize_display['kxsw_audiencesize'] = audiencesize_display['kxsw_audiencesize'].astype(int)\n",
    "audiencesize_display['fq_audiencesize'] = audiencesize_display['fq_audiencesize'].astype(int)\n",
    "audiencesize_display['fhq_audiencesize'] = audiencesize_display['fhq_audiencesize'].astype(int)\n",
    "audiencesize_display['greatfirewall_audiencesize'] = audiencesize_display['greatfirewall_audiencesize'].astype(int)"
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#fuckgfw</th>\n",
       "      <th>Ke Xue Shang Wang (科学上网)</th>\n",
       "      <th>Fan Qiang (翻墙)</th>\n",
       "      <th>Fang Huo Qiang （防火墙）</th>\n",
       "      <th>#greatfirewall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-25</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>437</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-26</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>358</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>445</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-28</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>419</td>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>322</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-30</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>447</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>551</td>\n",
       "      <td>71</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>342</td>\n",
       "      <td>138</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-02</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>605</td>\n",
       "      <td>101</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-03</th>\n",
       "      <td>47</td>\n",
       "      <td>10</td>\n",
       "      <td>413</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-04</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>387</td>\n",
       "      <td>65</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-05</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>394</td>\n",
       "      <td>181</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-06</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>432</td>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-07</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>288</td>\n",
       "      <td>22</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-08</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>333</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-09</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>234</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-11</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>287</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-12</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>328</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-13</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>425</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-14</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>266</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-15</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>267</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-16</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>202</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-17</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>248</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-18</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>347</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-19</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>418</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-20</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>283</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-21</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>313</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-22</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>338</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-23</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>219</td>\n",
       "      <td>49</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-24</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>240</td>\n",
       "      <td>22</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #fuckgfw  Ke Xue Shang Wang (科学上网)  Fan Qiang (翻墙)  \\\n",
       "2015-08-24         0                         1              35   \n",
       "2015-08-25         5                         2             437   \n",
       "2015-08-26         3                         6             358   \n",
       "2015-08-27        11                         6             445   \n",
       "2015-08-28         8                         5             419   \n",
       "2015-08-29         5                        16             322   \n",
       "2015-08-30         2                         6             447   \n",
       "2015-08-31         2                         2             551   \n",
       "2015-09-01        11                        22             342   \n",
       "2015-09-02         6                         4             605   \n",
       "2015-09-03        47                        10             413   \n",
       "2015-09-04         4                         6             387   \n",
       "2015-09-05         5                        20             394   \n",
       "2015-09-06         8                         5             432   \n",
       "2015-09-07         5                         0             288   \n",
       "2015-09-08         2                         7             333   \n",
       "2015-09-09         5                         5             234   \n",
       "2015-09-10         1                         3             204   \n",
       "2015-09-11         1                        10             287   \n",
       "2015-09-12         0                         3             328   \n",
       "2015-09-13         0                         6             425   \n",
       "2015-09-14         0                        39             266   \n",
       "2015-09-15         3                        13             267   \n",
       "2015-09-16         5                         5             202   \n",
       "2015-09-17         6                        11             248   \n",
       "2015-09-18         2                         7             347   \n",
       "2015-09-19         1                         2             418   \n",
       "2015-09-20         1                         2             283   \n",
       "2015-09-21         1                         5             313   \n",
       "2015-09-22         3                         3             338   \n",
       "2015-09-23         3                         3             219   \n",
       "2015-09-24         6                         2             240   \n",
       "\n",
       "            Fang Huo Qiang （防火墙）  #greatfirewall  \n",
       "2015-08-24                     0               0  \n",
       "2015-08-25                    26               2  \n",
       "2015-08-26                    19              11  \n",
       "2015-08-27                    54               3  \n",
       "2015-08-28                    49               9  \n",
       "2015-08-29                    19               0  \n",
       "2015-08-30                    18               7  \n",
       "2015-08-31                    71               6  \n",
       "2015-09-01                   138               4  \n",
       "2015-09-02                   101               6  \n",
       "2015-09-03                    45               1  \n",
       "2015-09-04                    65              36  \n",
       "2015-09-05                   181             124  \n",
       "2015-09-06                    59              62  \n",
       "2015-09-07                    22              81  \n",
       "2015-09-08                    18              34  \n",
       "2015-09-09                    33               9  \n",
       "2015-09-10                    16              10  \n",
       "2015-09-11                    57               3  \n",
       "2015-09-12                    56               1  \n",
       "2015-09-13                    24               2  \n",
       "2015-09-14                     8               5  \n",
       "2015-09-15                    23               6  \n",
       "2015-09-16                    23              14  \n",
       "2015-09-17                    23               5  \n",
       "2015-09-18                     9               3  \n",
       "2015-09-19                    32               1  \n",
       "2015-09-20                     4               3  \n",
       "2015-09-21                    45               2  \n",
       "2015-09-22                    59               3  \n",
       "2015-09-23                    49              19  \n",
       "2015-09-24                    22             130  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wayne\\Anaconda\\lib\\site-packages\\requests\\packages\\urllib3\\util\\ssl_.py:90: InsecurePlatformWarning:\n",
      "\n",
      "A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~cosmopolitanvan/145.embed\" height=\"430px\" width=\"850px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dailycount_forall\n",
    "dailycount_forall.columns = ['#fuckgfw', 'Ke Xue Shang Wang (科学上网)', \"Fan Qiang (翻墙)\", \"Fang Huo Qiang （防火墙）\", \"#greatfirewall\"]\n",
    "dailycount_forall\n",
    "\n",
    "# create Twitter Activity Chart\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import cufflinks as cf\n",
    "py.sign_in('cosmopolitanvan', 'jnv1ti79zi')\n",
    "\n",
    "layout = Layout(\n",
    "    title=\"Twitter Activity of Keywords/Hashtags related to China's Great Firewall\",\n",
    "    titlefont=Font(\n",
    "        family='Arial, sans-serif',\n",
    "        size=22,\n",
    "        color='black'\n",
    "    ),\n",
    "    font=Font(\n",
    "        family='\"Droid Sans\", sans-serif',\n",
    "        size=12\n",
    "    ),\n",
    "    showlegend=True,\n",
    "    autosize=False,\n",
    "    width=850,\n",
    "    height=430,\n",
    "    xaxis=XAxis(\n",
    "        title='',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[1440388800000, 1442462400000],\n",
    "        type='date',\n",
    "        autorange=True,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='inside',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    yaxis=YAxis(\n",
    "        title='# of tweets',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[-32.55555555555556, 638.5555555555555],\n",
    "        type='linear',\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    legend=Legend(\n",
    "        x=0.9013254786450663,\n",
    "        y=1.132,\n",
    "        traceorder='normal',\n",
    "        font=Font(\n",
    "            family='Arial, sans-serif',\n",
    "            size=12,\n",
    "            color='#666666'\n",
    "        ),\n",
    "        bgcolor='rgb(255, 255, 255)',\n",
    "        bordercolor='black',\n",
    "        borderwidth=0,\n",
    "        xref='paper',\n",
    "        yref='paper',\n",
    "        xanchor='left',\n",
    "        yanchor='top'\n",
    "    ),\n",
    "    paper_bgcolor='rgb(213, 226, 233)',\n",
    "    plot_bgcolor='rgb(213, 226, 233)',\n",
    "    bargap=0.2\n",
    ")\n",
    "\n",
    "cf.set_config_file(offline=False, world_readable=True, theme='ggplot')\n",
    "\n",
    "dailycount_forall.iplot(kind='scatter', layout = layout, filename='GFW/activity1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#fuckgfw</th>\n",
       "      <th>Ke Xue Shang Wang (科学上网)</th>\n",
       "      <th>Fan Qiang (翻墙)</th>\n",
       "      <th>Fang Huo Qiang （防火墙）</th>\n",
       "      <th>#greatfirewall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.628571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-25</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.894737</td>\n",
       "      <td>54.961538</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2015-08-26</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>11.843575</td>\n",
       "      <td>87.368421</td>\n",
       "      <td>1.363636</td>\n",
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       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.523596</td>\n",
       "      <td>58.425926</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-28</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.668258</td>\n",
       "      <td>41.081633</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-29</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>7.347826</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>24.375839</td>\n",
       "      <td>143.166667</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.531760</td>\n",
       "      <td>70.507042</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>9.272727</td>\n",
       "      <td>19.090909</td>\n",
       "      <td>13.140351</td>\n",
       "      <td>73.101449</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>37.479339</td>\n",
       "      <td>66.128713</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-03</th>\n",
       "      <td>29.489362</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>11.130751</td>\n",
       "      <td>24.222222</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>105.521964</td>\n",
       "      <td>566.169231</td>\n",
       "      <td>8.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-05</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>27.873096</td>\n",
       "      <td>119.270718</td>\n",
       "      <td>29.919355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-06</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.252315</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>44.790323</td>\n",
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       "    <tr>\n",
       "      <th>2015-09-07</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.458333</td>\n",
       "      <td>52.636364</td>\n",
       "      <td>18.604938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-08</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>10.225225</td>\n",
       "      <td>64.111111</td>\n",
       "      <td>13.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-09</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>77.786325</td>\n",
       "      <td>544.151515</td>\n",
       "      <td>2.222222</td>\n",
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       "    <tr>\n",
       "      <th>2015-09-10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>6.431373</td>\n",
       "      <td>0.375000</td>\n",
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       "      <th>2015-09-11</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>11.020906</td>\n",
       "      <td>43.368421</td>\n",
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       "    <tr>\n",
       "      <th>2015-09-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>64.792683</td>\n",
       "      <td>49.589286</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2015-09-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>51.856471</td>\n",
       "      <td>44.416667</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.769231</td>\n",
       "      <td>72.601504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-15</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>5.846154</td>\n",
       "      <td>16.606742</td>\n",
       "      <td>99.217391</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-16</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.034653</td>\n",
       "      <td>29.347826</td>\n",
       "      <td>4.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-17</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>15.262097</td>\n",
       "      <td>11.391304</td>\n",
       "      <td>27.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-18</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.054755</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>34.471292</td>\n",
       "      <td>40.656250</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-20</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>21.491166</td>\n",
       "      <td>42.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.322684</td>\n",
       "      <td>3.044444</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-22</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.571006</td>\n",
       "      <td>7.694915</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-23</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.949772</td>\n",
       "      <td>10.265306</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.083333</td>\n",
       "      <td>42.045455</td>\n",
       "      <td>0.046154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             #fuckgfw  Ke Xue Shang Wang (科学上网)  Fan Qiang (翻墙)  \\\n",
       "2015-08-24        NaN                  0.000000       37.628571   \n",
       "2015-08-25   0.200000                  0.000000       35.894737   \n",
       "2015-08-26  16.000000                  0.666667       11.843575   \n",
       "2015-08-27   0.727273                  3.000000       12.523596   \n",
       "2015-08-28   1.500000                  0.000000        9.668258   \n",
       "2015-08-29   0.600000                  2.500000        7.347826   \n",
       "2015-08-30   0.000000                  0.166667       24.375839   \n",
       "2015-08-31   0.000000                  0.000000       18.531760   \n",
       "2015-09-01   9.272727                 19.090909       13.140351   \n",
       "2015-09-02   0.000000                  5.500000       37.479339   \n",
       "2015-09-03  29.489362                  0.900000       11.130751   \n",
       "2015-09-04   0.000000                  0.666667      105.521964   \n",
       "2015-09-05  10.000000                 71.300000       27.873096   \n",
       "2015-09-06   0.875000                  0.000000       23.252315   \n",
       "2015-09-07   5.000000                       NaN        8.458333   \n",
       "2015-09-08   0.500000                  0.285714       10.225225   \n",
       "2015-09-09   0.200000                  0.200000       77.786325   \n",
       "2015-09-10   0.000000                  0.666667        6.431373   \n",
       "2015-09-11   1.000000                  6.300000       11.020906   \n",
       "2015-09-12        NaN                  6.000000       64.792683   \n",
       "2015-09-13        NaN                  2.666667       51.856471   \n",
       "2015-09-14        NaN                 16.769231       72.601504   \n",
       "2015-09-15   0.666667                  5.846154       16.606742   \n",
       "2015-09-16   2.400000                  0.000000        9.034653   \n",
       "2015-09-17   0.666667                  3.181818       15.262097   \n",
       "2015-09-18   0.500000                  0.000000       32.054755   \n",
       "2015-09-19   0.000000                 17.000000       34.471292   \n",
       "2015-09-20   1.000000                 17.500000       21.491166   \n",
       "2015-09-21   0.000000                  0.000000        8.322684   \n",
       "2015-09-22   0.333333                  0.000000        7.571006   \n",
       "2015-09-23   1.000000                  0.000000        4.949772   \n",
       "2015-09-24   0.000000                  0.000000        9.083333   \n",
       "\n",
       "            Fang Huo Qiang （防火墙）  #greatfirewall  \n",
       "2015-08-24                   NaN             NaN  \n",
       "2015-08-25             54.961538        0.000000  \n",
       "2015-08-26             87.368421        1.363636  \n",
       "2015-08-27             58.425926        0.000000  \n",
       "2015-08-28             41.081633        0.888889  \n",
       "2015-08-29              5.000000             NaN  \n",
       "2015-08-30            143.166667        0.428571  \n",
       "2015-08-31             70.507042        0.000000  \n",
       "2015-09-01             73.101449        0.500000  \n",
       "2015-09-02             66.128713        3.333333  \n",
       "2015-09-03             24.222222        0.000000  \n",
       "2015-09-04            566.169231        8.083333  \n",
       "2015-09-05            119.270718       29.919355  \n",
       "2015-09-06             62.000000       44.790323  \n",
       "2015-09-07             52.636364       18.604938  \n",
       "2015-09-08             64.111111       13.705882  \n",
       "2015-09-09            544.151515        2.222222  \n",
       "2015-09-10              0.375000       18.700000  \n",
       "2015-09-11             43.368421        0.666667  \n",
       "2015-09-12             49.589286        0.000000  \n",
       "2015-09-13             44.416667       72.000000  \n",
       "2015-09-14              0.000000        0.400000  \n",
       "2015-09-15             99.217391        4.666667  \n",
       "2015-09-16             29.347826        4.285714  \n",
       "2015-09-17             11.391304       27.600000  \n",
       "2015-09-18             19.000000        0.000000  \n",
       "2015-09-19             40.656250        0.000000  \n",
       "2015-09-20             42.750000        0.000000  \n",
       "2015-09-21              3.044444        0.000000  \n",
       "2015-09-22              7.694915        0.000000  \n",
       "2015-09-23             10.265306        0.421053  \n",
       "2015-09-24             42.045455        0.046154  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virality_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wayne\\Anaconda\\lib\\site-packages\\requests\\packages\\urllib3\\util\\ssl_.py:90: InsecurePlatformWarning:\n",
      "\n",
      "A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~cosmopolitanvan/151.embed\" height=\"430px\" width=\"850px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dailycount_forall\n",
    "virality_display.columns = ['#fuckgfw', 'Ke Xue Shang Wang (科学上网)', \"Fan Qiang (翻墙)\", \"Fang Huo Qiang （防火墙）\", \"#greatfirewall\"]\n",
    "\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import cufflinks as cf\n",
    "py.sign_in('cosmopolitanvan', 'jnv1ti79zi')\n",
    "\n",
    "layout = Layout(\n",
    "    title=\"Twitter Virality of Keywords/Hashtags related to China's Great Firewall\",\n",
    "    titlefont=Font(\n",
    "        family='Arial, sans-serif',\n",
    "        size=22,\n",
    "        color='black'\n",
    "    ),\n",
    "    font=Font(\n",
    "        family='\"Droid Sans\", sans-serif',\n",
    "        size=12\n",
    "    ),\n",
    "    showlegend=True,\n",
    "    autosize=False,\n",
    "    width=850,\n",
    "    height=430,\n",
    "    xaxis=XAxis(\n",
    "        title='',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[1440388800000, 1442462400000],\n",
    "        type='date',\n",
    "        autorange=True,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='inside',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    yaxis=YAxis(\n",
    "        title='Retweet count per tweet',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[-32.55555555555556, 638.5555555555555],\n",
    "        type='linear',\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=10,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    legend=Legend(\n",
    "        x=0.9013254786450663,\n",
    "        y=1.132,\n",
    "        traceorder='normal',\n",
    "        font=Font(\n",
    "            family='Arial, sans-serif',\n",
    "            size=12,\n",
    "            color='#666666'\n",
    "        ),\n",
    "        bgcolor='rgb(255, 255, 255)',\n",
    "        bordercolor='black',\n",
    "        borderwidth=0,\n",
    "        xref='paper',\n",
    "        yref='paper',\n",
    "        xanchor='left',\n",
    "        yanchor='top'\n",
    "    ),\n",
    "    paper_bgcolor='rgb(213, 226, 233)',\n",
    "    plot_bgcolor='rgb(213, 226, 233)',\n",
    "    bargap=0.2\n",
    ")\n",
    "\n",
    "cf.set_config_file(offline=False, world_readable=True, theme='ggplot')\n",
    "\n",
    "virality_display.iplot(kind='scatter', layout = layout, filename='GFW/virality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wayne\\Anaconda\\lib\\site-packages\\requests\\packages\\urllib3\\util\\ssl_.py:90: InsecurePlatformWarning:\n",
      "\n",
      "A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~cosmopolitanvan/147.embed\" height=\"430px\" width=\"850px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audiencesize_display.columns = ['#fuckgfw', 'Ke Xue Shang Wang (科学上网)', \"Fan Qiang (翻墙)\", \"Fang Huo Qiang （防火墙）\", \"#greatfirewall\"]\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import cufflinks as cf\n",
    "py.sign_in('cosmopolitanvan', 'jnv1ti79zi')\n",
    "\n",
    "layout = Layout(\n",
    "    title=\"Twitter Audience Size of Keywords/Hashtags related to China's Great Firewall\",\n",
    "    titlefont=Font(\n",
    "        family='Arial, sans-serif',\n",
    "        size=22,\n",
    "        color='black'\n",
    "    ),\n",
    "    font=Font(\n",
    "        family='\"Droid Sans\", sans-serif',\n",
    "        size=12\n",
    "    ),\n",
    "    showlegend=True,\n",
    "    autosize=False,\n",
    "    width=850,\n",
    "    height=430,\n",
    "    xaxis=XAxis(\n",
    "        title='',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[1440388800000, 1442462400000],\n",
    "        type='date',\n",
    "        autorange=True,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='inside',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    yaxis=YAxis(\n",
    "        title='Avg. follower count of discussant',\n",
    "        titlefont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        range=[-32.55555555555556, 638.5555555555555],\n",
    "        type='linear',\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=False,\n",
    "        showline=True,\n",
    "        nticks=5,\n",
    "        ticks='',\n",
    "        ticklen=6,\n",
    "        tickcolor='rgba(0, 0, 0, 0)',\n",
    "        tickfont=Font(\n",
    "            family='\"Verdana\", monospace',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        mirror=False,\n",
    "        gridcolor='white',\n",
    "        gridwidth=1,\n",
    "        zerolinecolor='#F6F6F6',\n",
    "        zerolinewidth=1,\n",
    "        linecolor='rgba(152, 0, 0, 0.5)',\n",
    "        linewidth=1.5\n",
    "    ),\n",
    "    legend=Legend(\n",
    "        x=1.0613254786450663,\n",
    "        y=1.132,\n",
    "        traceorder='normal',\n",
    "        font=Font(\n",
    "            family='Arial, sans-serif',\n",
    "            size=12,\n",
    "            color='#666666'\n",
    "        ),\n",
    "        bgcolor='rgb(255, 255, 255)',\n",
    "        bordercolor='black',\n",
    "        borderwidth=0,\n",
    "        xref='paper',\n",
    "        yref='paper',\n",
    "        xanchor='left',\n",
    "        yanchor='top'\n",
    "    ),\n",
    "    paper_bgcolor='rgb(213, 226, 233)',\n",
    "    plot_bgcolor='rgb(213, 226, 233)',\n",
    "    bargap=0.2\n",
    ")\n",
    "\n",
    "cf.set_config_file(offline=False, world_readable=True, theme='ggplot')\n",
    "\n",
    "audiencesize_display.iplot(kind='scatter', layout = layout, filename='GFW/audiencesize')\n"
   ]
  }
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