diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..12d120e
Binary files /dev/null and b/.DS_Store differ
diff --git a/your-code/.DS_Store b/your-code/.DS_Store
new file mode 100644
index 0000000..a870c7a
Binary files /dev/null and b/your-code/.DS_Store differ
diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
index 812f7a4..256b315 100644
--- a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
+++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,12 +48,13 @@
     "from bs4 import BeautifulSoup\n",
     "import pandas as pd\n",
     "# from pprint import pprint\n",
-    "# from lxml import html\n",
-    "# from lxml.html import fromstring\n",
+    "from lxml import html\n",
+    "from lxml.html import fromstring\n",
     "# import urllib.request\n",
     "# from urllib.request import urlopen\n",
-    "# import random\n",
-    "# import re\n",
+    "import random\n",
+    "import re\n",
+    "import html5lib\n",
     "# import scrapy"
    ]
   },
@@ -64,25 +65,6 @@
     "#### Download, parse (using BeautifulSoup), and print the content from the Trending Developers page from GitHub:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is the url you will scrape in this exercise\n",
-    "url = 'https://github.com/trending/developers'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#your code"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -134,11 +116,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://github.com/trending/developers'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "conts = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "git_soup = BeautifulSoup(conts, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 364,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "names = git_soup.find_all('h1', {\"class\":\"h3 lh-condensed\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nick = git_soup.find_all('a', {'class':\"link-gray\"})\n",
+    "nick = nick[19:44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "nicks = [i.text for i in nick]\n",
+    "names_s = [i.text for i in names]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Eric Ma (ericmjl)',\n",
+       " 'Federico Brigante (bfred-it)',\n",
+       " 'Kyle Roach (iRoachie)',\n",
+       " 'Olle Jonsson (olleolleolle)',\n",
+       " 'Nikita Sobolev (sobolevn)',\n",
+       " 'Frank S. Thomas (fthomas)',\n",
+       " 'syuilo (syuilo)',\n",
+       " 'Ives van Hoorne (CompuIves)',\n",
+       " 'Paulus Schoutsen (balloob)',\n",
+       " 'Sarah Drasner (sdras)',\n",
+       " 'Stefanos Kornilios Mitsis Poiitidis (skmp)',\n",
+       " 'Jan Hovancik (hovancik)',\n",
+       " 'Andreas Mueller (amueller)',\n",
+       " 'Guillaume Gomez (GuillaumeGomez)',\n",
+       " 'Matt Holt (mholt)',\n",
+       " 'Clifford Wolf (cliffordwolf)',\n",
+       " 'Franck Nijhof (frenck)',\n",
+       " 'Joe Block (unixorn)',\n",
+       " 'Andrei Neagoie (aneagoie)',\n",
+       " 'Jack Lloyd (randombit)',\n",
+       " 'Guillermo Rauch (rauchg)',\n",
+       " 'Tim Griesser (tgriesser)',\n",
+       " 'Jameson Nash (vtjnash)',\n",
+       " 'Anderson Banihirwe (andersy005)',\n",
+       " 'Danny Ryan (djrtwo)']"
+      ]
+     },
+     "execution_count": 367,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(len(names_s)):\n",
+    "    names_s[i] = names_s[i] +\" \"+ \"(\" + nicks[i] + \")\"\n",
+    "names_s"
    ]
   },
   {
@@ -152,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 368,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,11 +241,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 369,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "repos = requests.get(url).content\n",
+    "repo_soup = BeautifulSoup(repos, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "reposi = repo_soup.find_all('h1', {\"class\":\"h3 lh-condensed\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "repos = [i.text for i in reposi]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gto76/python-cheatsheet',\n",
+       " 'j3ssie/Osmedeus',\n",
+       " 'tangzixiang0304/Shielded_detector',\n",
+       " 'uber/ludwig',\n",
+       " 'xinshuoweng/AB3DMOT',\n",
+       " 'NVlabs/stylegan',\n",
+       " 'dagster-io/dagster',\n",
+       " 'tensorflow/models',\n",
+       " 'eragonruan/text-detection-ctpn',\n",
+       " 'sherlock-project/sherlock',\n",
+       " 'deepfakes/faceswap',\n",
+       " 'nbei/Deep-Flow-Guided-Video-Inpainting',\n",
+       " 'iovisor/bcc',\n",
+       " 'Roibal/Cryptocurrency-Trading-Bots-Python-Beginner-Advance',\n",
+       " 'NVIDIA/DeepLearningExamples',\n",
+       " 'BlackHC/tfpyth',\n",
+       " 'clovaai/deep-text-recognition-benchmark',\n",
+       " 'tkat0/PyTorch_BlazeFace',\n",
+       " 'OpenMined/PySyft',\n",
+       " 'CoreyMSchafer/code_snippets',\n",
+       " 'public-apis/public-apis',\n",
+       " 'd2l-ai/d2l-zh',\n",
+       " 'apache/airflow',\n",
+       " 'beecost/bee-university',\n",
+       " 'sundowndev/PhoneInfoga']"
+      ]
+     },
+     "execution_count": 372,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repos = [i.replace('\\n', '') for i in repos]\n",
+    "repos = [i.replace(' ', '') for i in repos]\n",
+    "repos"
    ]
   },
   {
@@ -178,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 373,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,11 +337,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 374,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "Walt = requests.get(url).content\n",
+    "walt_soup = BeautifulSoup(Walt, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "imgs = walt_soup.find_all('div', {\"class\":\"thumbinner\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/wiki/File:Walt_Disney_envelope_ca._1921.jpg',\n",
+       " '/wiki/File:Walt_Disney_envelope_ca._1921.jpg',\n",
+       " '//upload.wikimedia.org/wikipedia/commons/4/4d/Newman_Laugh-O-Gram_%281921%29.webm',\n",
+       " '/wiki/File:Newman_Laugh-O-Gram_(1921).webm',\n",
+       " '/wiki/File:Trolley_Troubles_poster.jpg',\n",
+       " '/wiki/File:Trolley_Troubles_poster.jpg',\n",
+       " '/wiki/Trolley_Troubles',\n",
+       " '/wiki/File:Walt_Disney_and_his_cartoon_creation_%22Mickey_Mouse%22_-_National_Board_of_Review_Magazine.jpg',\n",
+       " '/wiki/File:Walt_Disney_and_his_cartoon_creation_%22Mickey_Mouse%22_-_National_Board_of_Review_Magazine.jpg',\n",
+       " '/wiki/Mickey_Mouse',\n",
+       " '/wiki/File:Steamboat-willie.jpg',\n",
+       " '/wiki/File:Steamboat-willie.jpg',\n",
+       " '/wiki/Mickey_Mouse',\n",
+       " '/wiki/Steamboat_Willie',\n",
+       " '/wiki/File:Walt_Disney_1935.jpg',\n",
+       " '/wiki/File:Walt_Disney_1935.jpg',\n",
+       " '/wiki/File:Walt_Disney_Snow_white_1937_trailer_screenshot_(13).jpg',\n",
+       " '/wiki/File:Walt_Disney_Snow_white_1937_trailer_screenshot_(13).jpg',\n",
+       " '/wiki/Snow_White_and_the_Seven_Dwarfs_(1937_film)',\n",
+       " '/wiki/File:Disney_drawing_goofy.jpg',\n",
+       " '/wiki/File:Disney_drawing_goofy.jpg',\n",
+       " '/wiki/Goofy',\n",
+       " '/wiki/File:DisneySchiphol1951.jpg',\n",
+       " '/wiki/File:DisneySchiphol1951.jpg',\n",
+       " '/wiki/File:WaltDisneyplansDisneylandDec1954.jpg',\n",
+       " '/wiki/File:WaltDisneyplansDisneylandDec1954.jpg',\n",
+       " '/wiki/Disneyland',\n",
+       " '/wiki/Orange_County,_California',\n",
+       " '/wiki/File:Walt_disney_portrait_right.jpg',\n",
+       " '/wiki/File:Walt_disney_portrait_right.jpg',\n",
+       " '/wiki/File:Walt_Disney_Grave.JPG',\n",
+       " '/wiki/File:Walt_Disney_Grave.JPG',\n",
+       " '/wiki/File:Roy_O._Disney_with_Company_at_Press_Conference.jpg',\n",
+       " '/wiki/File:Roy_O._Disney_with_Company_at_Press_Conference.jpg',\n",
+       " '/wiki/Roy_O._Disney',\n",
+       " '/wiki/File:Disney_Display_Case.JPG',\n",
+       " '/wiki/File:Disney_Display_Case.JPG',\n",
+       " '/wiki/The_Walt_Disney_Family_Museum',\n",
+       " '/wiki/File:Disney1968.jpg',\n",
+       " '/wiki/File:Disney1968.jpg']"
+      ]
+     },
+     "execution_count": 376,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = []\n",
+    "for i in range(len(imgs)):\n",
+    "    for img in (imgs[i].find_all('a', href=True)):\n",
+    "                l.append(img['href'])\n",
+    "l"
    ]
   },
   {
@@ -209,7 +434,20 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url ='https://en.wikipedia.org/wiki/Python' "
+    "url ='https://en.wikipedia.org/wiki/Python' \n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "ul = soup.find_all('ul')\n",
+    "ul= ul[2:14]"
    ]
   },
   {
@@ -218,7 +456,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "l = [img['href'] for i in range(len(ul)) for img in (ul[i].find_all('a', href=True)) ] \n",
+    "l"
    ]
   },
   {
@@ -235,7 +474,8 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'http://uscode.house.gov/download/download.shtml'"
+    "url = 'http://uscode.house.gov/download/download.shtml'\n",
+    "html = requests.get(url).content"
    ]
   },
   {
@@ -244,7 +484,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "#soup\n",
+    "titles = soup.find_all('div', {'class':'usctitlechanged'})"
    ]
   },
   {
@@ -261,7 +504,19 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'https://www.fbi.gov/wanted/topten'"
+    "url = 'https://www.fbi.gov/wanted/topten'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code \n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "titles = soup.find_all('h3', {'class':'title'})"
    ]
   },
   {
@@ -270,7 +525,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code "
+    "buscados = [nombre.text.replace(\"\\n\", \"\") for nombre in titles]\n",
+    "buscados"
    ]
   },
   {
@@ -287,7 +543,60 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'https://www.emsc-csem.org/Earthquake/'"
+    "url = 'https://www.emsc-csem.org/Earthquake/'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "\n",
+    "datetime = soup.find_all('td', {'class':'tabev6'})\n",
+    "lyl = soup.find_all('td', {'class':'tabev1'})\n",
+    "region = soup.find_all('td', {'class':'tb_region'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limpdate =[str(fecha.text.replace('\\xa0', ' ')) for fecha in datetime]\n",
+    "datefull = []\n",
+    "for fecha in limpdate:\n",
+    "    fecha = fecha.replace('earthquake', '')\n",
+    "    fecha =fecha.replace('ago', '')\n",
+    "    datefull.append(fecha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lat =[]\n",
+    "lon = []\n",
+    "for latlon in range(len(lyl)):\n",
+    "    if latlon %2 == 0:\n",
+    "        lat.append(lyl[latlon].text.replace('\\xa0', ''))\n",
+    "    else:\n",
+    "        lon.append(lyl[latlon].text.replace('\\xa0', ''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lugar =[fecha.text for fecha in region]"
    ]
   },
   {
@@ -296,7 +605,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "df = pd.DataFrame({'Date and time':datefull, 'Latitud':lat, 'Longitud':lon, 'Region':lugar})\n",
+    "df.head(20)"
    ]
   },
   {
@@ -308,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 291,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,11 +628,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "hackatons_soup = requests.get(url).content\n",
+    "hackatons_soup = BeautifulSoup(hackatons_soup, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "hackis = hackatons_soup.find_all('div', {'class':'card-body'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Code Geist Hackathon by SefrWahed\\u20037/29/2019\\n\\n\\u2003Cairo, Egypt',\n",
+       " 'The Code Factor\\u20035/21/2019\\n\\n\\u2003Milano, Italy',\n",
+       " 'TECHFEST MUNICH\\u20039/6/2019\\n\\n\\u2003Munich, Germany',\n",
+       " 'Galileo App Competition\\u20031/31/2019\\n\\n\\u2003Prague, Czech Republic']"
+      ]
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hacks = [hackis[i].text for i in range(len(hackis))]\n",
+    "hacks = [i.split(\"\\u2003\") for i in hacks]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Code Geist Hackathon by SefrWahed</td>\n",
+       "      <td>7/29/2019</td>\n",
+       "      <td>Cairo, Egypt</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>The Code Factor</td>\n",
+       "      <td>5/21/2019</td>\n",
+       "      <td>Milano, Italy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TECHFEST MUNICH</td>\n",
+       "      <td>9/6/2019</td>\n",
+       "      <td>Munich, Germany</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Galileo App Competition</td>\n",
+       "      <td>1/31/2019</td>\n",
+       "      <td>Prague, Czech Republic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0          1                       2\n",
+       "0  Code Geist Hackathon by SefrWahed  7/29/2019            Cairo, Egypt\n",
+       "1                    The Code Factor  5/21/2019           Milano, Italy\n",
+       "2                    TECHFEST MUNICH   9/6/2019         Munich, Germany\n",
+       "3            Galileo App Competition  1/31/2019  Prague, Czech Republic"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hacks = pd.DataFrame(hacks)\n",
+    "hacks[1] = hacks[1].apply(lambda x:x.replace('\\n', ''))\n",
+    "hacks"
    ]
   },
   {
@@ -353,11 +777,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 300,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check the tweets of an account:shiroiusagi4486\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'76'"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your code"
+    "#your code\n",
+    "usr_input = input('Check the tweets of an account:')\n",
+    "try:\n",
+    "    twitter_follows = requests.get('https://twitter.com/'+usr_input).content\n",
+    "except:\n",
+    "    print('The account does not exist')\n",
+    "twitter_follows = BeautifulSoup(twitter_follows, 'lxml')\n",
+    "twitter_follows = twitter_follows.find_all('span', {'class': 'ProfileNav-value'})\n",
+    "twitter_follows[0]['data-count']\n",
+    "                                          \n",
+    "                                           "
    ]
   },
   {
@@ -388,11 +840,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 298,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check the followers of an account:uaquiro\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'302'"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your code"
+    "#your code\n",
+    "usr_input = input('Check the followers of an account:')\n",
+    "try:\n",
+    "    twitter_follows = requests.get('https://twitter.com/'+usr_input).content\n",
+    "except:\n",
+    "    print('The account does not exist')\n",
+    "twitter_follows = BeautifulSoup(twitter_follows, 'lxml')\n",
+    "twitter_follows = twitter_follows.find_all('span', {'class': 'ProfileNav-value'})\n",
+    "twitter_follows[2]['data-count']\n",
+    "                                          \n",
+    "                                           \n",
+    "                                          \n",
+    "                                    "
    ]
   },
   {
@@ -404,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,11 +896,243 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "wikissup = requests.get(url).content\n",
+    "wikissup = BeautifulSoup(wikissup, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "langs = wikissup.find_all('div', {'class':'central-featured'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['English',\n",
+       " '日本語',\n",
+       " 'Español',\n",
+       " 'Deutsch',\n",
+       " 'Русский',\n",
+       " 'Français',\n",
+       " 'Italiano',\n",
+       " '中文',\n",
+       " 'Português',\n",
+       " 'Polski']"
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "langu = []\n",
+    "for i in range(len(langs)):\n",
+    "    langu.append(langs[i].find_all('strong'))\n",
+    "langu = [i for i in langu[0]]\n",
+    "langu = [i.text for i in langu]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['English5\\xa0892\\xa0000',\n",
+       "  ' articles日本語1\\xa0159\\xa0000',\n",
+       "  ' 記事Español1\\xa0532\\xa0000',\n",
+       "  ' artículosDeutsch2\\xa0323\\xa0000',\n",
+       "  ' ArtikelРусский1\\xa0556\\xa0000',\n",
+       "  ' статейFrançais2\\xa0123\\xa0000',\n",
+       "  ' articlesItaliano1\\xa0541\\xa0000',\n",
+       "  ' voci中文1\\xa0065\\xa0000',\n",
+       "  ' 條目Português1\\xa0010\\xa0000',\n",
+       "  ' artigosPolski1\\xa0346\\xa0000',\n",
+       "  ' haseł']]"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [] \n",
+    "for i in range(len(langs)):\n",
+    "    numbers.append(langs[i].find_all('small'))\n",
+    "numbers = [i.text for i in langs]\n",
+    "numbers = [i.replace('\\n', '') for i in numbers]\n",
+    "numbers = [i.split('+') for i in numbers]\n",
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "numbers = [re.findall('\\d', i) for i in liss]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['5892000',\n",
+       " '1159000',\n",
+       " '1532000',\n",
+       " '2323000',\n",
+       " '1556000',\n",
+       " '2123000',\n",
+       " '1541000',\n",
+       " '1065000',\n",
+       " '1010000',\n",
+       " '1346000']"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numberss = [\"\".join(numbers[i]) for i in range(len(numbers))]\n",
+    "numbers = numberss[:-1]\n",
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Articles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>English</td>\n",
+       "      <td>5892000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>日本語</td>\n",
+       "      <td>1159000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Español</td>\n",
+       "      <td>1532000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Deutsch</td>\n",
+       "      <td>2323000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Русский</td>\n",
+       "      <td>1556000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Français</td>\n",
+       "      <td>2123000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Italiano</td>\n",
+       "      <td>1541000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>中文</td>\n",
+       "      <td>1065000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Português</td>\n",
+       "      <td>1010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Polski</td>\n",
+       "      <td>1346000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Language Articles\n",
+       "0    English  5892000\n",
+       "1        日本語  1159000\n",
+       "2    Español  1532000\n",
+       "3    Deutsch  2323000\n",
+       "4    Русский  1556000\n",
+       "5   Français  2123000\n",
+       "6   Italiano  1541000\n",
+       "7         中文  1065000\n",
+       "8  Português  1010000\n",
+       "9     Polski  1346000"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {'Language':langu, 'Articles':numbers}\n",
+    "Wiki_df = pd.DataFrame(d)\n",
+    "Wiki_df"
    ]
   },
   {
@@ -430,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,11 +1154,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code \n",
+    "datasets_soup = requests.get(url).content\n",
+    "datasets_soup = BeautifulSoup(datasets_soup, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Business and economy', 'Crime and justice', 'Defence', 'Education', 'Environment', 'Government', 'Government spending', 'Health', 'Mapping', 'Society', 'Towns and cities', 'Transport']\n"
+     ]
+    }
+   ],
+   "source": [
+    "names = datasets_soup.find_all('h2')\n",
+    "names_s1 = [i.text for i in names]\n",
+    "\n",
+    "print(names_s1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code "
+    "descp = datasets_soup.find_all('p')\n",
+    "descp = descp[5:-2]\n",
+    "descp = [i.text for i in descp]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Dataset</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Business and economy</td>\n",
+       "      <td>Small businesses, industry, imports, exports a...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Crime and justice</td>\n",
+       "      <td>Courts, police, prison, offenders, borders and...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Defence</td>\n",
+       "      <td>Armed forces, health and safety, search and re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Education</td>\n",
+       "      <td>Students, training, qualifications and the Nat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Environment</td>\n",
+       "      <td>Weather, flooding, rivers, air quality, geolog...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Government</td>\n",
+       "      <td>Staff numbers and pay, local councillors and d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Government spending</td>\n",
+       "      <td>Includes all payments by government department...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Health</td>\n",
+       "      <td>Includes smoking, drugs, alcohol, medicine per...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Mapping</td>\n",
+       "      <td>Addresses, boundaries, land ownership, aerial ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Society</td>\n",
+       "      <td>Employment, benefits, household finances, pove...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Towns and cities</td>\n",
+       "      <td>Includes housing, urban planning, leisure, was...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Transport</td>\n",
+       "      <td>Airports, roads, freight, electric vehicles, p...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Dataset                                        Description\n",
+       "0   Business and economy  Small businesses, industry, imports, exports a...\n",
+       "1      Crime and justice  Courts, police, prison, offenders, borders and...\n",
+       "2                Defence  Armed forces, health and safety, search and re...\n",
+       "3              Education  Students, training, qualifications and the Nat...\n",
+       "4            Environment  Weather, flooding, rivers, air quality, geolog...\n",
+       "5             Government  Staff numbers and pay, local councillors and d...\n",
+       "6    Government spending  Includes all payments by government department...\n",
+       "7                 Health  Includes smoking, drugs, alcohol, medicine per...\n",
+       "8                Mapping  Addresses, boundaries, land ownership, aerial ...\n",
+       "9                Society  Employment, benefits, household finances, pove...\n",
+       "10      Towns and cities  Includes housing, urban planning, leisure, was...\n",
+       "11             Transport  Airports, roads, freight, electric vehicles, p..."
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {'Dataset':names_s1, 'Description': descp}\n",
+    "DataSets = pd.DataFrame(d)\n",
+    "DataSets"
    ]
   },
   {
@@ -456,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,11 +1335,283 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "table_lang = requests.get(url).content\n",
+    "table_soup = BeautifulSoup(table_lang, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tables = table_soup.find_all('tr')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabless = []\n",
+    "for i in range(len(tables)):\n",
+    "    tabless.append(tables[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "tal = [tabless[i].text.split(\"\\n\\n\") for i in range(len(tabless))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rank</td>\n",
+       "      <td>Rank</td>\n",
+       "      <td>Language</td>\n",
+       "      <td>Primary Country</td>\n",
+       "      <td>TotalCountries[a]</td>\n",
+       "      <td>Speakers(millions)</td>\n",
+       "      <td>% of the World population\\n(March 2019)[7]</td>\n",
+       "      <td>\\nMacrolanguage</td>\n",
+       "      <td>Language familyBranch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>—</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Chinese (macrolanguage)</td>\n",
+       "      <td>China</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1,311</td>\n",
+       "      <td>17.026</td>\n",
+       "      <td></td>\n",
+       "      <td>Sino-TibetanSinitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>—</td>\n",
+       "      <td>Mandarin</td>\n",
+       "      <td>China</td>\n",
+       "      <td>13</td>\n",
+       "      <td>918</td>\n",
+       "      <td>11.922</td>\n",
+       "      <td>Chinese</td>\n",
+       "      <td>Sino-TibetanSinitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Spanish</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>31</td>\n",
+       "      <td>460</td>\n",
+       "      <td>5.974</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanRomance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>English</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>137</td>\n",
+       "      <td>379</td>\n",
+       "      <td>4.922</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanGermanic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Hindi</td>\n",
+       "      <td>India</td>\n",
+       "      <td>4</td>\n",
+       "      <td>341</td>\n",
+       "      <td>4.429</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanIndo-Aryan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>—</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Arabic (macrolanguage)</td>\n",
+       "      <td>Saudi Arabia</td>\n",
+       "      <td>59</td>\n",
+       "      <td>319</td>\n",
+       "      <td>4.143</td>\n",
+       "      <td></td>\n",
+       "      <td>AfroasiaticSemitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Bengali</td>\n",
+       "      <td>Bangladesh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>228</td>\n",
+       "      <td>2.961</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanIndo-Aryan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Portuguese</td>\n",
+       "      <td>Portugal</td>\n",
+       "      <td>15</td>\n",
+       "      <td>221</td>\n",
+       "      <td>2.870</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanRomance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Russian</td>\n",
+       "      <td>Russian Federation</td>\n",
+       "      <td>19</td>\n",
+       "      <td>154</td>\n",
+       "      <td>2.000</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanBalto-Slavic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Japanese</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>2</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td></td>\n",
+       "      <td>JaponicJapanese</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0     1                        2                   3  \\\n",
+       "0   Rank  Rank                 Language     Primary Country   \n",
+       "1      —     1  Chinese (macrolanguage)               China   \n",
+       "2      1     —                 Mandarin               China   \n",
+       "3      2     2                  Spanish               Spain   \n",
+       "4      3     3                  English      United Kingdom   \n",
+       "5      4     4                    Hindi               India   \n",
+       "6      —     5   Arabic (macrolanguage)        Saudi Arabia   \n",
+       "7      5     6                  Bengali          Bangladesh   \n",
+       "8      6     7               Portuguese            Portugal   \n",
+       "9      7     8                  Russian  Russian Federation   \n",
+       "10     8     9                 Japanese               Japan   \n",
+       "\n",
+       "                    4                   5  \\\n",
+       "0   TotalCountries[a]  Speakers(millions)   \n",
+       "1                  39               1,311   \n",
+       "2                  13                 918   \n",
+       "3                  31                 460   \n",
+       "4                 137                 379   \n",
+       "5                   4                 341   \n",
+       "6                  59                 319   \n",
+       "7                   4                 228   \n",
+       "8                  15                 221   \n",
+       "9                  19                 154   \n",
+       "10                  2                 128   \n",
+       "\n",
+       "                                             6                7  \\\n",
+       "0   % of the World population\\n(March 2019)[7]  \\nMacrolanguage   \n",
+       "1                                       17.026                    \n",
+       "2                                       11.922          Chinese   \n",
+       "3                                        5.974                    \n",
+       "4                                        4.922                    \n",
+       "5                                        4.429                    \n",
+       "6                                        4.143                    \n",
+       "7                                        2.961                    \n",
+       "8                                        2.870                    \n",
+       "9                                        2.000                    \n",
+       "10                                       1.662                    \n",
+       "\n",
+       "                            8  \n",
+       "0       Language familyBranch  \n",
+       "1         Sino-TibetanSinitic  \n",
+       "2         Sino-TibetanSinitic  \n",
+       "3        Indo-EuropeanRomance  \n",
+       "4       Indo-EuropeanGermanic  \n",
+       "5     Indo-EuropeanIndo-Aryan  \n",
+       "6          AfroasiaticSemitic  \n",
+       "7     Indo-EuropeanIndo-Aryan  \n",
+       "8        Indo-EuropeanRomance  \n",
+       "9   Indo-EuropeanBalto-Slavic  \n",
+       "10            JaponicJapanese  "
+      ]
+     },
+     "execution_count": 354,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "talcual = pd.DataFrame(tal)\n",
+    "talcual = talcual[:11]\n",
+    "talcual[0] = talcual[0].apply(lambda x: x.replace('\\n', ''))\n",
+    "talcual[8] = talcual[8].apply(lambda x: x.replace('\\n', ''))\n",
+    "talcual"
    ]
   },
   {
@@ -516,7 +1657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -530,7 +1671,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "fims_get = requests.get(url).content\n",
+    "films_soup = BeautifulSoup(films_get, 'lxml')\n",
+    "films_names = films_soup.find_all(<"
    ]
   },
   {
@@ -569,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 355,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,11 +1724,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 356,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "books = requests.get(url).content\n",
+    "books_soup = BeautifulSoup(books, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = books_soup.find_all('h3')\n",
+    "names_list = []\n",
+    "for i in range(len(names)):\n",
+    "    for name in (names[i].find_all('a', title=True)):\n",
+    "                names_list.append(name['title'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prices = books_soup.find_all('p', {'class':'price_color'})\n",
+    "\n",
+    "prices_list = [prices[i].text for i in range(len(prices))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock = books_soup.find_all('p', {'class':'instock availability'})\n",
+    "stock = [stock[i].text for i in range(len(stock))]\n",
+    "stock = [i.replace('\\n', \"\") for i in stock]\n",
+    "stock = [re.sub('\\s\\s+', '', stock[i]) for i in range(len(stock))] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Book Name</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Stock Availability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A Light in the Attic</td>\n",
+       "      <td>£51.77</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Tipping the Velvet</td>\n",
+       "      <td>£53.74</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Soumission</td>\n",
+       "      <td>£50.10</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sharp Objects</td>\n",
+       "      <td>£47.82</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sapiens: A Brief History of Humankind</td>\n",
+       "      <td>£54.23</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Book Name   Price Stock Availability\n",
+       "0                   A Light in the Attic  £51.77           In stock\n",
+       "1                     Tipping the Velvet  £53.74           In stock\n",
+       "2                             Soumission  £50.10           In stock\n",
+       "3                          Sharp Objects  £47.82           In stock\n",
+       "4  Sapiens: A Brief History of Humankind  £54.23           In stock"
+      ]
+     },
+     "execution_count": 360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {\"Book Name\":names_list, \"Price\":prices_list, \"Stock Availability\": stock}\n",
+    "books = pd.DataFrame(d)\n",
+    "books.head()"
    ]
   }
  ],
diff --git a/your-code/.ipynb_checkpoints/main_fer-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main_fer-checkpoint.ipynb
new file mode 100644
index 0000000..0e85fa9
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/main_fer-checkpoint.ipynb
@@ -0,0 +1,1224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Web Scraping Lab\n",
+    "\n",
+    "You will find in this notebook some scrapy exercises to practise your scraping skills.\n",
+    "\n",
+    "**Tips:**\n",
+    "\n",
+    "- Check the response status code for each request to ensure you have obtained the intended contennt.\n",
+    "- Print the response text in each request to understand the kind of info you are getting and its format.\n",
+    "- Check for patterns in the response text to extract the data/info requested in each question.\n",
+    "- Visit each url and take a look at its source through Chrome DevTools. You'll need to identify the html tags, special class names etc. used for the html content you are expected to extract."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Requests library](http://docs.python-requests.org/en/master/#the-user-guide) documentation \n",
+    "- [Beautiful Soup Doc](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)\n",
+    "- [Urllib](https://docs.python.org/3/library/urllib.html#module-urllib)\n",
+    "- [re lib](https://docs.python.org/3/library/re.html)\n",
+    "- [lxml lib](https://lxml.de/)\n",
+    "- [Scrapy](https://scrapy.org/)\n",
+    "- [List of HTTP status codes](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes)\n",
+    "- [HTML basics](http://www.simplehtmlguide.com/cheatsheet.php)\n",
+    "- [CSS basics](https://www.cssbasics.com/#page_start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Below are the libraries and modules you may need. `requests`,  `BeautifulSoup` and `pandas` are imported for you. If you prefer to use additional libraries feel free to uncomment them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "\n",
+    "# from pprint import pprint\n",
+    "from lxml import html\n",
+    "# from lxml.html import fromstring\n",
+    "# import urllib.request\n",
+    "# from urllib.request import urlopen\n",
+    "# import random\n",
+    "import re\n",
+    "# import scrapy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download, parse (using BeautifulSoup), and print the content from the Trending Developers page from GitHub:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://github.com/trending/developers'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the names of the trending developers retrieved in the previous step.\n",
+    "\n",
+    "Your output should be a Python list of developer names. Each name should not contain any html tag.\n",
+    "\n",
+    "**Instructions:**\n",
+    "\n",
+    "1. Find out the html tag and class names used for the developer names. You can achieve this using Chrome DevTools.\n",
+    "\n",
+    "1. Use BeautifulSoup to extract all the html elements that contain the developer names.\n",
+    "\n",
+    "1. Use string manipulation techniques to replace whitespaces and linebreaks (i.e. `\\n`) in the *text* of each html element. Use a list to store the clean names.\n",
+    "\n",
+    "1. Print the list of names.\n",
+    "\n",
+    "Your output should look like below:\n",
+    "\n",
+    "```\n",
+    "['trimstray (@trimstray)',\n",
+    " 'joewalnes (JoeWalnes)',\n",
+    " 'charlax (Charles-AxelDein)',\n",
+    " 'ForrestKnight (ForrestKnight)',\n",
+    " 'revery-ui (revery-ui)',\n",
+    " 'alibaba (Alibaba)',\n",
+    " 'Microsoft (Microsoft)',\n",
+    " 'github (GitHub)',\n",
+    " 'facebook (Facebook)',\n",
+    " 'boazsegev (Bo)',\n",
+    " 'google (Google)',\n",
+    " 'cloudfetch',\n",
+    " 'sindresorhus (SindreSorhus)',\n",
+    " 'tensorflow',\n",
+    " 'apache (TheApacheSoftwareFoundation)',\n",
+    " 'DevonCrawford (DevonCrawford)',\n",
+    " 'ARMmbed (ArmMbed)',\n",
+    " 'vuejs (vuejs)',\n",
+    " 'fastai (fast.ai)',\n",
+    " 'QiShaoXuan (Qi)',\n",
+    " 'joelparkerhenderson (JoelParkerHenderson)',\n",
+    " 'torvalds (LinusTorvalds)',\n",
+    " 'CyC2018',\n",
+    " 'komeiji-satori (神楽坂覚々)',\n",
+    " 'script-8']\n",
+    " ```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "h1 = soup.find_all('h1', {'class':'h3 lh-condensed'})\n",
+    "a = soup.find_all('a', {'class':'link-gray'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nombres = [link.string for link in h1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = a[19:44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nicks = [elemento.string for elemento in a]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "todo = [nombres[i] + \" \"+ '('+ nicks[i]+')' for i in range(len(nombres))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Eric Ma (ericmjl)',\n",
+       " 'Federico Brigante (bfred-it)',\n",
+       " 'Kyle Roach (iRoachie)',\n",
+       " 'Olle Jonsson (olleolleolle)',\n",
+       " 'Nikita Sobolev (sobolevn)',\n",
+       " 'Frank S. Thomas (fthomas)',\n",
+       " 'syuilo (syuilo)',\n",
+       " 'Ives van Hoorne (CompuIves)',\n",
+       " 'Paulus Schoutsen (balloob)',\n",
+       " 'Sarah Drasner (sdras)',\n",
+       " 'Stefanos Kornilios Mitsis Poiitidis (skmp)',\n",
+       " 'Jan Hovancik (hovancik)',\n",
+       " 'Andreas Mueller (amueller)',\n",
+       " 'Guillaume Gomez (GuillaumeGomez)',\n",
+       " 'Matt Holt (mholt)',\n",
+       " 'Clifford Wolf (cliffordwolf)',\n",
+       " 'Franck Nijhof (frenck)',\n",
+       " 'Joe Block (unixorn)',\n",
+       " 'Andrei Neagoie (aneagoie)',\n",
+       " 'Jack Lloyd (randombit)',\n",
+       " 'Guillermo Rauch (rauchg)',\n",
+       " 'Tim Griesser (tgriesser)',\n",
+       " 'Jameson Nash (vtjnash)',\n",
+       " 'Anderson Banihirwe (andersy005)',\n",
+       " 'Danny Ryan (djrtwo)']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "todo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the trending Python repositories in GitHub\n",
+    "\n",
+    "The steps to solve this problem is similar to the previous one except that you need to find out the repository names instead of developer names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://github.com/trending/python?since=daily'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "h1 = soup.find_all('h1', {'class':'h3 lh-condensed'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proyectos = [algo.text.replace(\"\\n\", \"\") for algo in h1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gto76 / python-cheatsheet ',\n",
+       " 'j3ssie / Osmedeus ',\n",
+       " 'tangzixiang0304 / Shielded_detector ',\n",
+       " 'uber / ludwig ',\n",
+       " 'xinshuoweng / AB3DMOT ',\n",
+       " 'NVlabs / stylegan ',\n",
+       " 'dagster-io / dagster ',\n",
+       " 'tensorflow / models ',\n",
+       " 'eragonruan / text-detection-ctpn ',\n",
+       " 'sherlock-project / sherlock ',\n",
+       " 'deepfakes / faceswap ',\n",
+       " 'nbei / Deep-Flow-Guided-Video-Inpainting ',\n",
+       " 'iovisor / bcc ',\n",
+       " 'Roibal / Cryptocurrency-Trading-Bots-Python-Beginner-Advance ',\n",
+       " 'NVIDIA / DeepLearningExamples ',\n",
+       " 'BlackHC / tfpyth ',\n",
+       " 'clovaai / deep-text-recognition-benchmark ',\n",
+       " 'tkat0 / PyTorch_BlazeFace ',\n",
+       " 'OpenMined / PySyft ',\n",
+       " 'CoreyMSchafer / code_snippets ',\n",
+       " 'public-apis / public-apis ',\n",
+       " 'd2l-ai / d2l-zh ',\n",
+       " 'apache / airflow ',\n",
+       " 'beecost / bee-university ',\n",
+       " 'sundowndev / PhoneInfoga ']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "proyectos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display all the image links from Walt Disney wikipedia page"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://en.wikipedia.org/wiki/Walt_Disney'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve an arbitary Wikipedia page of \"Python\" and create a list of links on that page"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url ='https://en.wikipedia.org/wiki/Python'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<ul><li><a href=\"/wiki/Pythonidae\" title=\"Pythonidae\">Pythonidae</a>, a family of nonvenomous snakes found in Africa, Asia, and Australia known as pythons\n",
+       " <ul><li><a href=\"/wiki/Python_(genus)\" title=\"Python (genus)\"><i>Python</i> (genus)</a>, of nonvenomous Pythonidae found in Africa and Asia</li></ul></li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(genus)\" title=\"Python (genus)\"><i>Python</i> (genus)</a>, of nonvenomous Pythonidae found in Africa and Asia</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(mythology)\" title=\"Python (mythology)\">Python (mythology)</a>, a serpent, the earth-dragon of Delphi</li>\n",
+       " <li><a href=\"/wiki/Python_of_Aenus\" title=\"Python of Aenus\">Python of Aenus</a> (4th-century BCE), student of Plato</li>\n",
+       " <li><a href=\"/wiki/Python_(painter)\" title=\"Python (painter)\">Python (painter)</a>, (ca. 360-320 BCE) vase painter in Poseidonia</li>\n",
+       " <li><a href=\"/wiki/Python_of_Byzantium\" title=\"Python of Byzantium\">Python of Byzantium</a>, orator, diplomat of Philip II of Macedon</li>\n",
+       " <li><a href=\"/wiki/Python_of_Catana\" title=\"Python of Catana\">Python of Catana</a>, poet who accompanied Alexander the Great</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(film)\" title=\"Python (film)\"><i>Python</i> (film)</a>, a 2000 horror film by Richard Clabaugh\n",
+       " <ul><li><i><a href=\"/wiki/Pythons_2\" title=\"Pythons 2\">Pythons 2</a></i>, or <i>Python II</i>, a 2002 sequel to <i>Python</i></li></ul></li>\n",
+       " <li>The Pythons, or <a href=\"/wiki/Monty_Python\" title=\"Monty Python\">Monty Python</a>, a British comedy group\n",
+       " <ul><li><a href=\"/wiki/Python_(Monty)_Pictures\" title=\"Python (Monty) Pictures\">Python (Monty) Pictures</a>, a company owned by the troupe's surviving members</li></ul></li></ul>,\n",
+       " <ul><li><i><a href=\"/wiki/Pythons_2\" title=\"Pythons 2\">Pythons 2</a></i>, or <i>Python II</i>, a 2002 sequel to <i>Python</i></li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(Monty)_Pictures\" title=\"Python (Monty) Pictures\">Python (Monty) Pictures</a>, a company owned by the troupe's surviving members</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(programming_language)\" title=\"Python (programming language)\">Python (programming language)</a>\n",
+       " <ul><li><a href=\"/wiki/CPython\" title=\"CPython\">CPython</a>, the reference implementation of the Python programming language</li></ul></li>\n",
+       " <li>Python, a native code compiler for <a href=\"/wiki/CMU_Common_Lisp\" title=\"CMU Common Lisp\">CMU Common Lisp</a></li>\n",
+       " <li>Python, the internal project name for the <a href=\"/wiki/PERQ#PERQ_3\" title=\"PERQ\">PERQ 3</a> computer workstation</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/CPython\" title=\"CPython\">CPython</a>, the reference implementation of the Python programming language</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(Busch_Gardens_Tampa_Bay)\" title=\"Python (Busch Gardens Tampa Bay)\">Python (Busch Gardens Tampa Bay)</a>, a defunct roller coaster at Busch Gardens Africa</li>\n",
+       " <li><a href=\"/wiki/Python_(Coney_Island,_Cincinnati,_Ohio)\" title=\"Python (Coney Island, Cincinnati, Ohio)\">Python (Coney Island, Cincinnati, Ohio)</a>, a D.P.V. Rides designed Zyklon-style steel roller coaster that operated from 1996 to 1999 at Splash Zone Water Park but was relocated to Coney Island at the end of the 1999 season</li>\n",
+       " <li><a href=\"/wiki/Python_(Efteling)\" title=\"Python (Efteling)\">Python (Efteling)</a>, a roller coaster in Efteling, the Netherlands</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_(automobile_maker)\" title=\"Python (automobile maker)\">Python (automobile maker)</a>, an Australian car company</li>\n",
+       " <li><a href=\"/wiki/Python_(Ford_prototype)\" title=\"Python (Ford prototype)\">Python (Ford prototype)</a>, a Ford prototype sports car</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Colt_Python\" title=\"Colt Python\">Colt Python</a>, a revolver</li>\n",
+       " <li><a href=\"/wiki/Python_(missile)\" title=\"Python (missile)\">Python (missile)</a>, a series of Israeli air-to-air missiles</li>\n",
+       " <li><a href=\"/wiki/Python_(nuclear_primary)\" title=\"Python (nuclear primary)\">Python (nuclear primary)</a>, a gas-boosted fission primary used in thermonuclear weapons</li></ul>,\n",
+       " <ul><li><a href=\"/wiki/Python_Anghelo\" title=\"Python Anghelo\">Python Anghelo</a> (1954–2014) Romanian graphic artist</li></ul>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "ul = soup.find_all('ul')\n",
+    "ul= ul[2:14]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/wiki/Pythonidae',\n",
+       " '/wiki/Python_(genus)',\n",
+       " '/wiki/Python_(genus)',\n",
+       " '/wiki/Python_(mythology)',\n",
+       " '/wiki/Python_of_Aenus',\n",
+       " '/wiki/Python_(painter)',\n",
+       " '/wiki/Python_of_Byzantium',\n",
+       " '/wiki/Python_of_Catana',\n",
+       " '/wiki/Python_(film)',\n",
+       " '/wiki/Pythons_2',\n",
+       " '/wiki/Monty_Python',\n",
+       " '/wiki/Python_(Monty)_Pictures',\n",
+       " '/wiki/Pythons_2',\n",
+       " '/wiki/Python_(Monty)_Pictures',\n",
+       " '/wiki/Python_(programming_language)',\n",
+       " '/wiki/CPython',\n",
+       " '/wiki/CMU_Common_Lisp',\n",
+       " '/wiki/PERQ#PERQ_3',\n",
+       " '/wiki/CPython',\n",
+       " '/wiki/Python_(Busch_Gardens_Tampa_Bay)',\n",
+       " '/wiki/Python_(Coney_Island,_Cincinnati,_Ohio)',\n",
+       " '/wiki/Python_(Efteling)',\n",
+       " '/wiki/Python_(automobile_maker)',\n",
+       " '/wiki/Python_(Ford_prototype)',\n",
+       " '/wiki/Colt_Python',\n",
+       " '/wiki/Python_(missile)',\n",
+       " '/wiki/Python_(nuclear_primary)',\n",
+       " '/wiki/Python_Anghelo']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = [img['href'] for i in range(len(ul)) for img in (ul[i].find_all('a', href=True)) ] \n",
+    "l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of Titles that have changed in the United States Code since its last release point "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'http://uscode.house.gov/download/download.shtml'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "#soup\n",
+    "titles = soup.find_all('div', {'class':'usctitlechanged'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(titles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A Python list with the top ten FBI's Most Wanted names "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://www.fbi.gov/wanted/topten'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code \n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "titles = soup.find_all('h3', {'class':'title'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ALEJANDRO ROSALES CASTILLO',\n",
+       " 'YASER ABDEL SAID',\n",
+       " 'JASON DEREK BROWN',\n",
+       " 'RAFAEL CARO-QUINTERO',\n",
+       " 'ALEXIS FLORES',\n",
+       " 'EUGENE PALMER',\n",
+       " 'SANTIAGO VILLALBA MEDEROS',\n",
+       " 'ROBERT WILLIAM FISHER',\n",
+       " 'BHADRESHKUMAR CHETANBHAI PATEL',\n",
+       " 'ARNOLDO JIMENEZ']"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "buscados = [nombre.text.replace(\"\\n\", \"\") for nombre in titles]\n",
+    "buscados"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  20 latest earthquakes info (date, time, latitude, longitude and region name) by the EMSC as a pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://www.emsc-csem.org/Earthquake/'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "\n",
+    "datetime = soup.find_all('td', {'class':'tabev6'})\n",
+    "lyl = soup.find_all('td', {'class':'tabev1'})\n",
+    "region = soup.find_all('td', {'class':'tb_region'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limpdate =[str(fecha.text.replace('\\xa0', ' ')) for fecha in datetime]\n",
+    "datefull = []\n",
+    "for fecha in limpdate:\n",
+    "    fecha = fecha.replace('earthquake', '')\n",
+    "    fecha =fecha.replace('ago', '')\n",
+    "    datefull.append(fecha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lat =[]\n",
+    "lon = []\n",
+    "for latlon in range(len(lyl)):\n",
+    "    if latlon %2 == 0:\n",
+    "        lat.append(lyl[latlon].text.replace('\\xa0', ''))\n",
+    "    else:\n",
+    "        lon.append(lyl[latlon].text.replace('\\xa0', ''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lugar =[fecha.text for fecha in region]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'Date and time':datefull, 'Latitud':lat, 'Longitud':lon, 'Region':lugar})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date and time</th>\n",
+       "      <th>Latitud</th>\n",
+       "      <th>Longitud</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-07-16   21:06:21.515min</td>\n",
+       "      <td>35.88</td>\n",
+       "      <td>117.69</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-07-16   21:03:36.718min</td>\n",
+       "      <td>37.81</td>\n",
+       "      <td>121.76</td>\n",
+       "      <td>SAN FRANCISCO BAY AREA, CALIF.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-07-16   20:52:18.629min</td>\n",
+       "      <td>36.07</td>\n",
+       "      <td>117.84</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-07-16   20:49:09.132min</td>\n",
+       "      <td>36.07</td>\n",
+       "      <td>117.65</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-07-16   20:41:11.040min</td>\n",
+       "      <td>16.85</td>\n",
+       "      <td>100.25</td>\n",
+       "      <td>OFFSHORE GUERRERO, MEXICO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2019-07-16   20:33:52.948min</td>\n",
+       "      <td>40.09</td>\n",
+       "      <td>19.91</td>\n",
+       "      <td>ALBANIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2019-07-16   20:31:33.350min</td>\n",
+       "      <td>23.45</td>\n",
+       "      <td>66.86</td>\n",
+       "      <td>JUJUY, ARGENTINA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2019-07-16   20:29:07.752min</td>\n",
+       "      <td>35.86</td>\n",
+       "      <td>117.69</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2019-07-16   20:23:34.758min</td>\n",
+       "      <td>36.07</td>\n",
+       "      <td>117.84</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2019-07-16   20:19:00.11hr 02min</td>\n",
+       "      <td>33.10</td>\n",
+       "      <td>12.42</td>\n",
+       "      <td>MADEIRA ISLANDS, PORTUGAL REGION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2019-07-16   20:17:51.61hr 04min</td>\n",
+       "      <td>35.55</td>\n",
+       "      <td>117.43</td>\n",
+       "      <td>SOUTHERN CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019-07-16   20:15:59.01hr 05min</td>\n",
+       "      <td>35.68</td>\n",
+       "      <td>117.52</td>\n",
+       "      <td>SOUTHERN CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2019-07-16   20:11:01.51hr 10min</td>\n",
+       "      <td>37.82</td>\n",
+       "      <td>121.77</td>\n",
+       "      <td>SAN FRANCISCO BAY AREA, CALIF.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2019-07-16   19:51:06.51hr 30min</td>\n",
+       "      <td>6.26</td>\n",
+       "      <td>148.65</td>\n",
+       "      <td>NEW BRITAIN REGION, P.N.G.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2019-07-16   19:42:25.91hr 39min</td>\n",
+       "      <td>35.61</td>\n",
+       "      <td>117.47</td>\n",
+       "      <td>SOUTHERN CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2019-07-16   19:35:57.01hr 46min</td>\n",
+       "      <td>35.62</td>\n",
+       "      <td>117.45</td>\n",
+       "      <td>SOUTHERN CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2019-07-16   19:23:50.11hr 58min</td>\n",
+       "      <td>36.19</td>\n",
+       "      <td>117.89</td>\n",
+       "      <td>CENTRAL CALIFORNIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>2019-07-16   19:20:21.42hr 01min</td>\n",
+       "      <td>38.39</td>\n",
+       "      <td>16.94</td>\n",
+       "      <td>SOUTHERN ITALY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2019-07-16   19:16:53.82hr 05min</td>\n",
+       "      <td>38.45</td>\n",
+       "      <td>16.91</td>\n",
+       "      <td>SOUTHERN ITALY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2019-07-16   19:16:15.92hr 05min</td>\n",
+       "      <td>61.27</td>\n",
+       "      <td>152.44</td>\n",
+       "      <td>SOUTHERN ALASKA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Date and time Latitud Longitud  \\\n",
+       "0       2019-07-16   21:06:21.515min    35.88   117.69   \n",
+       "1       2019-07-16   21:03:36.718min    37.81   121.76   \n",
+       "2       2019-07-16   20:52:18.629min    36.07   117.84   \n",
+       "3       2019-07-16   20:49:09.132min    36.07   117.65   \n",
+       "4       2019-07-16   20:41:11.040min    16.85   100.25   \n",
+       "5       2019-07-16   20:33:52.948min    40.09    19.91   \n",
+       "6       2019-07-16   20:31:33.350min    23.45    66.86   \n",
+       "7       2019-07-16   20:29:07.752min    35.86   117.69   \n",
+       "8       2019-07-16   20:23:34.758min    36.07   117.84   \n",
+       "9   2019-07-16   20:19:00.11hr 02min    33.10    12.42   \n",
+       "10  2019-07-16   20:17:51.61hr 04min    35.55   117.43   \n",
+       "11  2019-07-16   20:15:59.01hr 05min    35.68   117.52   \n",
+       "12  2019-07-16   20:11:01.51hr 10min    37.82   121.77   \n",
+       "13  2019-07-16   19:51:06.51hr 30min     6.26   148.65   \n",
+       "14  2019-07-16   19:42:25.91hr 39min    35.61   117.47   \n",
+       "15  2019-07-16   19:35:57.01hr 46min    35.62   117.45   \n",
+       "16  2019-07-16   19:23:50.11hr 58min    36.19   117.89   \n",
+       "17  2019-07-16   19:20:21.42hr 01min    38.39    16.94   \n",
+       "18  2019-07-16   19:16:53.82hr 05min    38.45    16.91   \n",
+       "19  2019-07-16   19:16:15.92hr 05min    61.27   152.44   \n",
+       "\n",
+       "                               Region  \n",
+       "0                  CENTRAL CALIFORNIA  \n",
+       "1      SAN FRANCISCO BAY AREA, CALIF.  \n",
+       "2                  CENTRAL CALIFORNIA  \n",
+       "3                  CENTRAL CALIFORNIA  \n",
+       "4           OFFSHORE GUERRERO, MEXICO  \n",
+       "5                             ALBANIA  \n",
+       "6                    JUJUY, ARGENTINA  \n",
+       "7                  CENTRAL CALIFORNIA  \n",
+       "8                  CENTRAL CALIFORNIA  \n",
+       "9    MADEIRA ISLANDS, PORTUGAL REGION  \n",
+       "10                SOUTHERN CALIFORNIA  \n",
+       "11                SOUTHERN CALIFORNIA  \n",
+       "12     SAN FRANCISCO BAY AREA, CALIF.  \n",
+       "13         NEW BRITAIN REGION, P.N.G.  \n",
+       "14                SOUTHERN CALIFORNIA  \n",
+       "15                SOUTHERN CALIFORNIA  \n",
+       "16                 CENTRAL CALIFORNIA  \n",
+       "17                     SOUTHERN ITALY  \n",
+       "18                     SOUTHERN ITALY  \n",
+       "19                    SOUTHERN ALASKA  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the date, and title of upcoming hackathon events as a Pandas dataframe table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url ='https://hackevents.co/hackathons'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Count number of tweets by a given Twitter account."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will need to include a ***try/except block*** for account names not found. \n",
+    "<br>***Hint:*** the program should count the number of tweets for any provided account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise \n",
+    "# You will need to add the account credentials to this url\n",
+    "url = 'https://twitter.com/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of followers of a given twitter account"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will need to include a ***try/except block*** in case account/s name not found. \n",
+    "<br>***Hint:*** the program should count the followers for any provided account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise \n",
+    "# You will need to add the account credentials to this url\n",
+    "url = 'https://twitter.com/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### List all language names and number of related articles in the order they appear in wikipedia.org"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://www.wikipedia.org/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A list with the different kind of datasets available in data.gov.uk "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://data.gov.uk/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Top 10 languages by number of native speakers stored in a Pandas Dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BONUS QUESTIONS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Scrape a certain number of tweets of a given Twitter account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise \n",
+    "# You will need to add the account credentials to this url\n",
+    "url = 'https://twitter.com/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### IMDB's Top 250 data (movie name, Initial release, director name and stars) as a pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise \n",
+    "url = 'https://www.imdb.com/chart/top'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Find the live weather report (temperature, wind speed, description and weather) of a given city."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://openweathermap.org/current\n",
+    "city = city=input('Enter the city:')\n",
+    "url = 'http://api.openweathermap.org/data/2.5/weather?'+'q='+city+'&APPID=b35975e18dc93725acb092f7272cc6b8&units=metric'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Book name, price and stock availability as a pandas dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise. \n",
+    "# It is a fictional bookstore created to be scraped. \n",
+    "url = 'http://books.toscrape.com/'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "\n",
+    "datetime = soup.find_all('td', {'class':'tabev6'})\n",
+    "lyl = soup.find_all('td', {'class':'tabev1'})\n",
+    "region = soup.find_all('td', {'class':'tb_region'})\n",
+    "\n",
+    "limpdate =[str(fecha.text.replace('\\xa0', ' ')) for fecha in datetime]\n",
+    "datefull = []\n",
+    "for fecha in limpdate:\n",
+    "    fecha = fecha.replace('earthquake', '')\n",
+    "    fecha =fecha.replace('ago', '')\n",
+    "    datefull.append(fecha)\n",
+    "    \n",
+    "lat =[]\n",
+    "lon = []\n",
+    "for latlon in range(len(lyl)):\n",
+    "    if latlon %2 == 0:\n",
+    "        lat.append(lyl[latlon].text.replace('\\xa0', ''))\n",
+    "    else:\n",
+    "        lon.append(lyl[latlon].text.replace('\\xa0', ''))\n",
+    "        \n",
+    "lugar =[fecha.text for fecha in region]\n",
+    "df = pd.DataFrame({'Date and time':datefull, 'Latitud':lat, 'Longitud':lon, 'Region':lugar})"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 812f7a4..256b315 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,12 +48,13 @@
     "from bs4 import BeautifulSoup\n",
     "import pandas as pd\n",
     "# from pprint import pprint\n",
-    "# from lxml import html\n",
-    "# from lxml.html import fromstring\n",
+    "from lxml import html\n",
+    "from lxml.html import fromstring\n",
     "# import urllib.request\n",
     "# from urllib.request import urlopen\n",
-    "# import random\n",
-    "# import re\n",
+    "import random\n",
+    "import re\n",
+    "import html5lib\n",
     "# import scrapy"
    ]
   },
@@ -64,25 +65,6 @@
     "#### Download, parse (using BeautifulSoup), and print the content from the Trending Developers page from GitHub:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is the url you will scrape in this exercise\n",
-    "url = 'https://github.com/trending/developers'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#your code"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -134,11 +116,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the url you will scrape in this exercise\n",
+    "url = 'https://github.com/trending/developers'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "conts = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "git_soup = BeautifulSoup(conts, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 364,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "names = git_soup.find_all('h1', {\"class\":\"h3 lh-condensed\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nick = git_soup.find_all('a', {'class':\"link-gray\"})\n",
+    "nick = nick[19:44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "nicks = [i.text for i in nick]\n",
+    "names_s = [i.text for i in names]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Eric Ma (ericmjl)',\n",
+       " 'Federico Brigante (bfred-it)',\n",
+       " 'Kyle Roach (iRoachie)',\n",
+       " 'Olle Jonsson (olleolleolle)',\n",
+       " 'Nikita Sobolev (sobolevn)',\n",
+       " 'Frank S. Thomas (fthomas)',\n",
+       " 'syuilo (syuilo)',\n",
+       " 'Ives van Hoorne (CompuIves)',\n",
+       " 'Paulus Schoutsen (balloob)',\n",
+       " 'Sarah Drasner (sdras)',\n",
+       " 'Stefanos Kornilios Mitsis Poiitidis (skmp)',\n",
+       " 'Jan Hovancik (hovancik)',\n",
+       " 'Andreas Mueller (amueller)',\n",
+       " 'Guillaume Gomez (GuillaumeGomez)',\n",
+       " 'Matt Holt (mholt)',\n",
+       " 'Clifford Wolf (cliffordwolf)',\n",
+       " 'Franck Nijhof (frenck)',\n",
+       " 'Joe Block (unixorn)',\n",
+       " 'Andrei Neagoie (aneagoie)',\n",
+       " 'Jack Lloyd (randombit)',\n",
+       " 'Guillermo Rauch (rauchg)',\n",
+       " 'Tim Griesser (tgriesser)',\n",
+       " 'Jameson Nash (vtjnash)',\n",
+       " 'Anderson Banihirwe (andersy005)',\n",
+       " 'Danny Ryan (djrtwo)']"
+      ]
+     },
+     "execution_count": 367,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range(len(names_s)):\n",
+    "    names_s[i] = names_s[i] +\" \"+ \"(\" + nicks[i] + \")\"\n",
+    "names_s"
    ]
   },
   {
@@ -152,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 368,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,11 +241,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 369,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "repos = requests.get(url).content\n",
+    "repo_soup = BeautifulSoup(repos, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "reposi = repo_soup.find_all('h1', {\"class\":\"h3 lh-condensed\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "repos = [i.text for i in reposi]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gto76/python-cheatsheet',\n",
+       " 'j3ssie/Osmedeus',\n",
+       " 'tangzixiang0304/Shielded_detector',\n",
+       " 'uber/ludwig',\n",
+       " 'xinshuoweng/AB3DMOT',\n",
+       " 'NVlabs/stylegan',\n",
+       " 'dagster-io/dagster',\n",
+       " 'tensorflow/models',\n",
+       " 'eragonruan/text-detection-ctpn',\n",
+       " 'sherlock-project/sherlock',\n",
+       " 'deepfakes/faceswap',\n",
+       " 'nbei/Deep-Flow-Guided-Video-Inpainting',\n",
+       " 'iovisor/bcc',\n",
+       " 'Roibal/Cryptocurrency-Trading-Bots-Python-Beginner-Advance',\n",
+       " 'NVIDIA/DeepLearningExamples',\n",
+       " 'BlackHC/tfpyth',\n",
+       " 'clovaai/deep-text-recognition-benchmark',\n",
+       " 'tkat0/PyTorch_BlazeFace',\n",
+       " 'OpenMined/PySyft',\n",
+       " 'CoreyMSchafer/code_snippets',\n",
+       " 'public-apis/public-apis',\n",
+       " 'd2l-ai/d2l-zh',\n",
+       " 'apache/airflow',\n",
+       " 'beecost/bee-university',\n",
+       " 'sundowndev/PhoneInfoga']"
+      ]
+     },
+     "execution_count": 372,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repos = [i.replace('\\n', '') for i in repos]\n",
+    "repos = [i.replace(' ', '') for i in repos]\n",
+    "repos"
    ]
   },
   {
@@ -178,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 373,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,11 +337,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 374,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "Walt = requests.get(url).content\n",
+    "walt_soup = BeautifulSoup(Walt, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "imgs = walt_soup.find_all('div', {\"class\":\"thumbinner\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/wiki/File:Walt_Disney_envelope_ca._1921.jpg',\n",
+       " '/wiki/File:Walt_Disney_envelope_ca._1921.jpg',\n",
+       " '//upload.wikimedia.org/wikipedia/commons/4/4d/Newman_Laugh-O-Gram_%281921%29.webm',\n",
+       " '/wiki/File:Newman_Laugh-O-Gram_(1921).webm',\n",
+       " '/wiki/File:Trolley_Troubles_poster.jpg',\n",
+       " '/wiki/File:Trolley_Troubles_poster.jpg',\n",
+       " '/wiki/Trolley_Troubles',\n",
+       " '/wiki/File:Walt_Disney_and_his_cartoon_creation_%22Mickey_Mouse%22_-_National_Board_of_Review_Magazine.jpg',\n",
+       " '/wiki/File:Walt_Disney_and_his_cartoon_creation_%22Mickey_Mouse%22_-_National_Board_of_Review_Magazine.jpg',\n",
+       " '/wiki/Mickey_Mouse',\n",
+       " '/wiki/File:Steamboat-willie.jpg',\n",
+       " '/wiki/File:Steamboat-willie.jpg',\n",
+       " '/wiki/Mickey_Mouse',\n",
+       " '/wiki/Steamboat_Willie',\n",
+       " '/wiki/File:Walt_Disney_1935.jpg',\n",
+       " '/wiki/File:Walt_Disney_1935.jpg',\n",
+       " '/wiki/File:Walt_Disney_Snow_white_1937_trailer_screenshot_(13).jpg',\n",
+       " '/wiki/File:Walt_Disney_Snow_white_1937_trailer_screenshot_(13).jpg',\n",
+       " '/wiki/Snow_White_and_the_Seven_Dwarfs_(1937_film)',\n",
+       " '/wiki/File:Disney_drawing_goofy.jpg',\n",
+       " '/wiki/File:Disney_drawing_goofy.jpg',\n",
+       " '/wiki/Goofy',\n",
+       " '/wiki/File:DisneySchiphol1951.jpg',\n",
+       " '/wiki/File:DisneySchiphol1951.jpg',\n",
+       " '/wiki/File:WaltDisneyplansDisneylandDec1954.jpg',\n",
+       " '/wiki/File:WaltDisneyplansDisneylandDec1954.jpg',\n",
+       " '/wiki/Disneyland',\n",
+       " '/wiki/Orange_County,_California',\n",
+       " '/wiki/File:Walt_disney_portrait_right.jpg',\n",
+       " '/wiki/File:Walt_disney_portrait_right.jpg',\n",
+       " '/wiki/File:Walt_Disney_Grave.JPG',\n",
+       " '/wiki/File:Walt_Disney_Grave.JPG',\n",
+       " '/wiki/File:Roy_O._Disney_with_Company_at_Press_Conference.jpg',\n",
+       " '/wiki/File:Roy_O._Disney_with_Company_at_Press_Conference.jpg',\n",
+       " '/wiki/Roy_O._Disney',\n",
+       " '/wiki/File:Disney_Display_Case.JPG',\n",
+       " '/wiki/File:Disney_Display_Case.JPG',\n",
+       " '/wiki/The_Walt_Disney_Family_Museum',\n",
+       " '/wiki/File:Disney1968.jpg',\n",
+       " '/wiki/File:Disney1968.jpg']"
+      ]
+     },
+     "execution_count": 376,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = []\n",
+    "for i in range(len(imgs)):\n",
+    "    for img in (imgs[i].find_all('a', href=True)):\n",
+    "                l.append(img['href'])\n",
+    "l"
    ]
   },
   {
@@ -209,7 +434,20 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url ='https://en.wikipedia.org/wiki/Python' "
+    "url ='https://en.wikipedia.org/wiki/Python' \n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "ul = soup.find_all('ul')\n",
+    "ul= ul[2:14]"
    ]
   },
   {
@@ -218,7 +456,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "l = [img['href'] for i in range(len(ul)) for img in (ul[i].find_all('a', href=True)) ] \n",
+    "l"
    ]
   },
   {
@@ -235,7 +474,8 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'http://uscode.house.gov/download/download.shtml'"
+    "url = 'http://uscode.house.gov/download/download.shtml'\n",
+    "html = requests.get(url).content"
    ]
   },
   {
@@ -244,7 +484,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "#soup\n",
+    "titles = soup.find_all('div', {'class':'usctitlechanged'})"
    ]
   },
   {
@@ -261,7 +504,19 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'https://www.fbi.gov/wanted/topten'"
+    "url = 'https://www.fbi.gov/wanted/topten'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code \n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "titles = soup.find_all('h3', {'class':'title'})"
    ]
   },
   {
@@ -270,7 +525,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code "
+    "buscados = [nombre.text.replace(\"\\n\", \"\") for nombre in titles]\n",
+    "buscados"
    ]
   },
   {
@@ -287,7 +543,60 @@
    "outputs": [],
    "source": [
     "# This is the url you will scrape in this exercise\n",
-    "url = 'https://www.emsc-csem.org/Earthquake/'"
+    "url = 'https://www.emsc-csem.org/Earthquake/'\n",
+    "html = requests.get(url).content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "soup = BeautifulSoup(html, 'lxml')\n",
+    "\n",
+    "datetime = soup.find_all('td', {'class':'tabev6'})\n",
+    "lyl = soup.find_all('td', {'class':'tabev1'})\n",
+    "region = soup.find_all('td', {'class':'tb_region'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limpdate =[str(fecha.text.replace('\\xa0', ' ')) for fecha in datetime]\n",
+    "datefull = []\n",
+    "for fecha in limpdate:\n",
+    "    fecha = fecha.replace('earthquake', '')\n",
+    "    fecha =fecha.replace('ago', '')\n",
+    "    datefull.append(fecha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lat =[]\n",
+    "lon = []\n",
+    "for latlon in range(len(lyl)):\n",
+    "    if latlon %2 == 0:\n",
+    "        lat.append(lyl[latlon].text.replace('\\xa0', ''))\n",
+    "    else:\n",
+    "        lon.append(lyl[latlon].text.replace('\\xa0', ''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lugar =[fecha.text for fecha in region]"
    ]
   },
   {
@@ -296,7 +605,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "df = pd.DataFrame({'Date and time':datefull, 'Latitud':lat, 'Longitud':lon, 'Region':lugar})\n",
+    "df.head(20)"
    ]
   },
   {
@@ -308,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 291,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,11 +628,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "hackatons_soup = requests.get(url).content\n",
+    "hackatons_soup = BeautifulSoup(hackatons_soup, 'html5lib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "hackis = hackatons_soup.find_all('div', {'class':'card-body'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Code Geist Hackathon by SefrWahed\\u20037/29/2019\\n\\n\\u2003Cairo, Egypt',\n",
+       " 'The Code Factor\\u20035/21/2019\\n\\n\\u2003Milano, Italy',\n",
+       " 'TECHFEST MUNICH\\u20039/6/2019\\n\\n\\u2003Munich, Germany',\n",
+       " 'Galileo App Competition\\u20031/31/2019\\n\\n\\u2003Prague, Czech Republic']"
+      ]
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hacks = [hackis[i].text for i in range(len(hackis))]\n",
+    "hacks = [i.split(\"\\u2003\") for i in hacks]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Code Geist Hackathon by SefrWahed</td>\n",
+       "      <td>7/29/2019</td>\n",
+       "      <td>Cairo, Egypt</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>The Code Factor</td>\n",
+       "      <td>5/21/2019</td>\n",
+       "      <td>Milano, Italy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TECHFEST MUNICH</td>\n",
+       "      <td>9/6/2019</td>\n",
+       "      <td>Munich, Germany</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Galileo App Competition</td>\n",
+       "      <td>1/31/2019</td>\n",
+       "      <td>Prague, Czech Republic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0          1                       2\n",
+       "0  Code Geist Hackathon by SefrWahed  7/29/2019            Cairo, Egypt\n",
+       "1                    The Code Factor  5/21/2019           Milano, Italy\n",
+       "2                    TECHFEST MUNICH   9/6/2019         Munich, Germany\n",
+       "3            Galileo App Competition  1/31/2019  Prague, Czech Republic"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hacks = pd.DataFrame(hacks)\n",
+    "hacks[1] = hacks[1].apply(lambda x:x.replace('\\n', ''))\n",
+    "hacks"
    ]
   },
   {
@@ -353,11 +777,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 300,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check the tweets of an account:shiroiusagi4486\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'76'"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your code"
+    "#your code\n",
+    "usr_input = input('Check the tweets of an account:')\n",
+    "try:\n",
+    "    twitter_follows = requests.get('https://twitter.com/'+usr_input).content\n",
+    "except:\n",
+    "    print('The account does not exist')\n",
+    "twitter_follows = BeautifulSoup(twitter_follows, 'lxml')\n",
+    "twitter_follows = twitter_follows.find_all('span', {'class': 'ProfileNav-value'})\n",
+    "twitter_follows[0]['data-count']\n",
+    "                                          \n",
+    "                                           "
    ]
   },
   {
@@ -388,11 +840,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 298,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check the followers of an account:uaquiro\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'302'"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your code"
+    "#your code\n",
+    "usr_input = input('Check the followers of an account:')\n",
+    "try:\n",
+    "    twitter_follows = requests.get('https://twitter.com/'+usr_input).content\n",
+    "except:\n",
+    "    print('The account does not exist')\n",
+    "twitter_follows = BeautifulSoup(twitter_follows, 'lxml')\n",
+    "twitter_follows = twitter_follows.find_all('span', {'class': 'ProfileNav-value'})\n",
+    "twitter_follows[2]['data-count']\n",
+    "                                          \n",
+    "                                           \n",
+    "                                          \n",
+    "                                    "
    ]
   },
   {
@@ -404,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,11 +896,243 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "wikissup = requests.get(url).content\n",
+    "wikissup = BeautifulSoup(wikissup, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "langs = wikissup.find_all('div', {'class':'central-featured'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['English',\n",
+       " '日本語',\n",
+       " 'Español',\n",
+       " 'Deutsch',\n",
+       " 'Русский',\n",
+       " 'Français',\n",
+       " 'Italiano',\n",
+       " '中文',\n",
+       " 'Português',\n",
+       " 'Polski']"
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "langu = []\n",
+    "for i in range(len(langs)):\n",
+    "    langu.append(langs[i].find_all('strong'))\n",
+    "langu = [i for i in langu[0]]\n",
+    "langu = [i.text for i in langu]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['English5\\xa0892\\xa0000',\n",
+       "  ' articles日本語1\\xa0159\\xa0000',\n",
+       "  ' 記事Español1\\xa0532\\xa0000',\n",
+       "  ' artículosDeutsch2\\xa0323\\xa0000',\n",
+       "  ' ArtikelРусский1\\xa0556\\xa0000',\n",
+       "  ' статейFrançais2\\xa0123\\xa0000',\n",
+       "  ' articlesItaliano1\\xa0541\\xa0000',\n",
+       "  ' voci中文1\\xa0065\\xa0000',\n",
+       "  ' 條目Português1\\xa0010\\xa0000',\n",
+       "  ' artigosPolski1\\xa0346\\xa0000',\n",
+       "  ' haseł']]"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [] \n",
+    "for i in range(len(langs)):\n",
+    "    numbers.append(langs[i].find_all('small'))\n",
+    "numbers = [i.text for i in langs]\n",
+    "numbers = [i.replace('\\n', '') for i in numbers]\n",
+    "numbers = [i.split('+') for i in numbers]\n",
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "numbers = [re.findall('\\d', i) for i in liss]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['5892000',\n",
+       " '1159000',\n",
+       " '1532000',\n",
+       " '2323000',\n",
+       " '1556000',\n",
+       " '2123000',\n",
+       " '1541000',\n",
+       " '1065000',\n",
+       " '1010000',\n",
+       " '1346000']"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numberss = [\"\".join(numbers[i]) for i in range(len(numbers))]\n",
+    "numbers = numberss[:-1]\n",
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Articles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>English</td>\n",
+       "      <td>5892000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>日本語</td>\n",
+       "      <td>1159000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Español</td>\n",
+       "      <td>1532000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Deutsch</td>\n",
+       "      <td>2323000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Русский</td>\n",
+       "      <td>1556000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Français</td>\n",
+       "      <td>2123000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Italiano</td>\n",
+       "      <td>1541000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>中文</td>\n",
+       "      <td>1065000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Português</td>\n",
+       "      <td>1010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Polski</td>\n",
+       "      <td>1346000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Language Articles\n",
+       "0    English  5892000\n",
+       "1        日本語  1159000\n",
+       "2    Español  1532000\n",
+       "3    Deutsch  2323000\n",
+       "4    Русский  1556000\n",
+       "5   Français  2123000\n",
+       "6   Italiano  1541000\n",
+       "7         中文  1065000\n",
+       "8  Português  1010000\n",
+       "9     Polski  1346000"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {'Language':langu, 'Articles':numbers}\n",
+    "Wiki_df = pd.DataFrame(d)\n",
+    "Wiki_df"
    ]
   },
   {
@@ -430,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,11 +1154,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code \n",
+    "datasets_soup = requests.get(url).content\n",
+    "datasets_soup = BeautifulSoup(datasets_soup, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Business and economy', 'Crime and justice', 'Defence', 'Education', 'Environment', 'Government', 'Government spending', 'Health', 'Mapping', 'Society', 'Towns and cities', 'Transport']\n"
+     ]
+    }
+   ],
+   "source": [
+    "names = datasets_soup.find_all('h2')\n",
+    "names_s1 = [i.text for i in names]\n",
+    "\n",
+    "print(names_s1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code "
+    "descp = datasets_soup.find_all('p')\n",
+    "descp = descp[5:-2]\n",
+    "descp = [i.text for i in descp]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Dataset</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Business and economy</td>\n",
+       "      <td>Small businesses, industry, imports, exports a...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Crime and justice</td>\n",
+       "      <td>Courts, police, prison, offenders, borders and...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Defence</td>\n",
+       "      <td>Armed forces, health and safety, search and re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Education</td>\n",
+       "      <td>Students, training, qualifications and the Nat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Environment</td>\n",
+       "      <td>Weather, flooding, rivers, air quality, geolog...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Government</td>\n",
+       "      <td>Staff numbers and pay, local councillors and d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Government spending</td>\n",
+       "      <td>Includes all payments by government department...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Health</td>\n",
+       "      <td>Includes smoking, drugs, alcohol, medicine per...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Mapping</td>\n",
+       "      <td>Addresses, boundaries, land ownership, aerial ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Society</td>\n",
+       "      <td>Employment, benefits, household finances, pove...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Towns and cities</td>\n",
+       "      <td>Includes housing, urban planning, leisure, was...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Transport</td>\n",
+       "      <td>Airports, roads, freight, electric vehicles, p...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Dataset                                        Description\n",
+       "0   Business and economy  Small businesses, industry, imports, exports a...\n",
+       "1      Crime and justice  Courts, police, prison, offenders, borders and...\n",
+       "2                Defence  Armed forces, health and safety, search and re...\n",
+       "3              Education  Students, training, qualifications and the Nat...\n",
+       "4            Environment  Weather, flooding, rivers, air quality, geolog...\n",
+       "5             Government  Staff numbers and pay, local councillors and d...\n",
+       "6    Government spending  Includes all payments by government department...\n",
+       "7                 Health  Includes smoking, drugs, alcohol, medicine per...\n",
+       "8                Mapping  Addresses, boundaries, land ownership, aerial ...\n",
+       "9                Society  Employment, benefits, household finances, pove...\n",
+       "10      Towns and cities  Includes housing, urban planning, leisure, was...\n",
+       "11             Transport  Airports, roads, freight, electric vehicles, p..."
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {'Dataset':names_s1, 'Description': descp}\n",
+    "DataSets = pd.DataFrame(d)\n",
+    "DataSets"
    ]
   },
   {
@@ -456,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,11 +1335,283 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code\n",
+    "table_lang = requests.get(url).content\n",
+    "table_soup = BeautifulSoup(table_lang, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tables = table_soup.find_all('tr')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tabless = []\n",
+    "for i in range(len(tables)):\n",
+    "    tabless.append(tables[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "tal = [tabless[i].text.split(\"\\n\\n\") for i in range(len(tabless))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rank</td>\n",
+       "      <td>Rank</td>\n",
+       "      <td>Language</td>\n",
+       "      <td>Primary Country</td>\n",
+       "      <td>TotalCountries[a]</td>\n",
+       "      <td>Speakers(millions)</td>\n",
+       "      <td>% of the World population\\n(March 2019)[7]</td>\n",
+       "      <td>\\nMacrolanguage</td>\n",
+       "      <td>Language familyBranch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>—</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Chinese (macrolanguage)</td>\n",
+       "      <td>China</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1,311</td>\n",
+       "      <td>17.026</td>\n",
+       "      <td></td>\n",
+       "      <td>Sino-TibetanSinitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>—</td>\n",
+       "      <td>Mandarin</td>\n",
+       "      <td>China</td>\n",
+       "      <td>13</td>\n",
+       "      <td>918</td>\n",
+       "      <td>11.922</td>\n",
+       "      <td>Chinese</td>\n",
+       "      <td>Sino-TibetanSinitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Spanish</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>31</td>\n",
+       "      <td>460</td>\n",
+       "      <td>5.974</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanRomance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>English</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>137</td>\n",
+       "      <td>379</td>\n",
+       "      <td>4.922</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanGermanic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Hindi</td>\n",
+       "      <td>India</td>\n",
+       "      <td>4</td>\n",
+       "      <td>341</td>\n",
+       "      <td>4.429</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanIndo-Aryan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>—</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Arabic (macrolanguage)</td>\n",
+       "      <td>Saudi Arabia</td>\n",
+       "      <td>59</td>\n",
+       "      <td>319</td>\n",
+       "      <td>4.143</td>\n",
+       "      <td></td>\n",
+       "      <td>AfroasiaticSemitic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Bengali</td>\n",
+       "      <td>Bangladesh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>228</td>\n",
+       "      <td>2.961</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanIndo-Aryan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Portuguese</td>\n",
+       "      <td>Portugal</td>\n",
+       "      <td>15</td>\n",
+       "      <td>221</td>\n",
+       "      <td>2.870</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanRomance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Russian</td>\n",
+       "      <td>Russian Federation</td>\n",
+       "      <td>19</td>\n",
+       "      <td>154</td>\n",
+       "      <td>2.000</td>\n",
+       "      <td></td>\n",
+       "      <td>Indo-EuropeanBalto-Slavic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Japanese</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>2</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td></td>\n",
+       "      <td>JaponicJapanese</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0     1                        2                   3  \\\n",
+       "0   Rank  Rank                 Language     Primary Country   \n",
+       "1      —     1  Chinese (macrolanguage)               China   \n",
+       "2      1     —                 Mandarin               China   \n",
+       "3      2     2                  Spanish               Spain   \n",
+       "4      3     3                  English      United Kingdom   \n",
+       "5      4     4                    Hindi               India   \n",
+       "6      —     5   Arabic (macrolanguage)        Saudi Arabia   \n",
+       "7      5     6                  Bengali          Bangladesh   \n",
+       "8      6     7               Portuguese            Portugal   \n",
+       "9      7     8                  Russian  Russian Federation   \n",
+       "10     8     9                 Japanese               Japan   \n",
+       "\n",
+       "                    4                   5  \\\n",
+       "0   TotalCountries[a]  Speakers(millions)   \n",
+       "1                  39               1,311   \n",
+       "2                  13                 918   \n",
+       "3                  31                 460   \n",
+       "4                 137                 379   \n",
+       "5                   4                 341   \n",
+       "6                  59                 319   \n",
+       "7                   4                 228   \n",
+       "8                  15                 221   \n",
+       "9                  19                 154   \n",
+       "10                  2                 128   \n",
+       "\n",
+       "                                             6                7  \\\n",
+       "0   % of the World population\\n(March 2019)[7]  \\nMacrolanguage   \n",
+       "1                                       17.026                    \n",
+       "2                                       11.922          Chinese   \n",
+       "3                                        5.974                    \n",
+       "4                                        4.922                    \n",
+       "5                                        4.429                    \n",
+       "6                                        4.143                    \n",
+       "7                                        2.961                    \n",
+       "8                                        2.870                    \n",
+       "9                                        2.000                    \n",
+       "10                                       1.662                    \n",
+       "\n",
+       "                            8  \n",
+       "0       Language familyBranch  \n",
+       "1         Sino-TibetanSinitic  \n",
+       "2         Sino-TibetanSinitic  \n",
+       "3        Indo-EuropeanRomance  \n",
+       "4       Indo-EuropeanGermanic  \n",
+       "5     Indo-EuropeanIndo-Aryan  \n",
+       "6          AfroasiaticSemitic  \n",
+       "7     Indo-EuropeanIndo-Aryan  \n",
+       "8        Indo-EuropeanRomance  \n",
+       "9   Indo-EuropeanBalto-Slavic  \n",
+       "10            JaponicJapanese  "
+      ]
+     },
+     "execution_count": 354,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "talcual = pd.DataFrame(tal)\n",
+    "talcual = talcual[:11]\n",
+    "talcual[0] = talcual[0].apply(lambda x: x.replace('\\n', ''))\n",
+    "talcual[8] = talcual[8].apply(lambda x: x.replace('\\n', ''))\n",
+    "talcual"
    ]
   },
   {
@@ -516,7 +1657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -530,7 +1671,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "fims_get = requests.get(url).content\n",
+    "films_soup = BeautifulSoup(films_get, 'lxml')\n",
+    "films_names = films_soup.find_all(<"
    ]
   },
   {
@@ -569,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 355,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,11 +1724,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 356,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code"
+    "#your code\n",
+    "books = requests.get(url).content\n",
+    "books_soup = BeautifulSoup(books, 'html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = books_soup.find_all('h3')\n",
+    "names_list = []\n",
+    "for i in range(len(names)):\n",
+    "    for name in (names[i].find_all('a', title=True)):\n",
+    "                names_list.append(name['title'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prices = books_soup.find_all('p', {'class':'price_color'})\n",
+    "\n",
+    "prices_list = [prices[i].text for i in range(len(prices))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock = books_soup.find_all('p', {'class':'instock availability'})\n",
+    "stock = [stock[i].text for i in range(len(stock))]\n",
+    "stock = [i.replace('\\n', \"\") for i in stock]\n",
+    "stock = [re.sub('\\s\\s+', '', stock[i]) for i in range(len(stock))] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Book Name</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Stock Availability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A Light in the Attic</td>\n",
+       "      <td>£51.77</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Tipping the Velvet</td>\n",
+       "      <td>£53.74</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Soumission</td>\n",
+       "      <td>£50.10</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sharp Objects</td>\n",
+       "      <td>£47.82</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sapiens: A Brief History of Humankind</td>\n",
+       "      <td>£54.23</td>\n",
+       "      <td>In stock</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Book Name   Price Stock Availability\n",
+       "0                   A Light in the Attic  £51.77           In stock\n",
+       "1                     Tipping the Velvet  £53.74           In stock\n",
+       "2                             Soumission  £50.10           In stock\n",
+       "3                          Sharp Objects  £47.82           In stock\n",
+       "4  Sapiens: A Brief History of Humankind  £54.23           In stock"
+      ]
+     },
+     "execution_count": 360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {\"Book Name\":names_list, \"Price\":prices_list, \"Stock Availability\": stock}\n",
+    "books = pd.DataFrame(d)\n",
+    "books.head()"
    ]
   }
  ],