SF_crawl_analysis.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Python Pandas Screaming Frog notebook\n",
    "\n",
    "Analyzing a Screaming Frog crawl with Python Pandas and a bunch of other stuff.  \n",
    "Check out [this article](https://www.databulle.com/blog/code/crawl-analysis-in-python.html) for more info.  \n",
    "\n",
    "Make sure you installed the dependencies: simply run `pip install -r requirements.txt` to install.  \n",
    "\n",
    "\n",
    "You'll need two exports from Screaming Frog:  \n",
    "- internal_all.csv  \n",
    "- all_outlinks.csv  \n",
    "\n",
    "Place them in the `data` directory.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br />\n",
    "<br />\n",
    "Let's import the Pandas library.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Display plots directly in the notebook\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now import our crawl data directly in two Pandas dataframes:  \n",
    "- one (`urls`) for URLs data,  \n",
    "- and the second (`links`) for links.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Status Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Title 1</th>\n",
       "      <th>Meta Description 1</th>\n",
       "      <th>H1-1</th>\n",
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       "      <th>Meta Robots 1</th>\n",
       "      <th>Meta Refresh 1</th>\n",
       "      <th>Canonical Link Element 1</th>\n",
       "      <th>Word Count</th>\n",
       "      <th>Text Ratio</th>\n",
       "      <th>Crawl Depth</th>\n",
       "      <th>Inlinks</th>\n",
       "      <th>Unique Inlinks</th>\n",
       "      <th>Outlinks</th>\n",
       "      <th>Unique Outlinks</th>\n",
       "      <th>External Outlinks</th>\n",
       "      <th>Unique External Outlinks</th>\n",
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       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>Django Girls Ibagué | Python.org</td>\n",
       "      <td>The official home of the Python Programming La...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Django Girls Ibagué</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335</td>\n",
       "      <td>6.423675</td>\n",
       "      <td>2</td>\n",
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       "      <td>15</td>\n",
       "      <td>113</td>\n",
       "      <td>45</td>\n",
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       "      <td>https://www.python.org/dev/peps/pep-0471/</td>\n",
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       "      <th>3</th>\n",
       "      <td>https://www.python.org/m/files/f990-2001.pdf</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://www.python.org/ftp/python/3.5.0/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
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      "text/plain": [
       "                                             Address  Status Code  \\\n",
       "0           https://www.python.org/dev/peps/pep-0470          301   \n",
       "1  https://www.python.org/events/python-user-grou...          200   \n",
       "2           https://www.python.org/dev/peps/pep-0471          301   \n",
       "3       https://www.python.org/m/files/f990-2001.pdf          200   \n",
       "4  https://www.python.org/ftp/python/3.5.0/python...          200   \n",
       "\n",
       "              Status                           Title 1  \\\n",
       "0  MOVED PERMANENTLY                               NaN   \n",
       "1                 OK  Django Girls Ibagué | Python.org   \n",
       "2  MOVED PERMANENTLY                               NaN   \n",
       "3                 OK                               NaN   \n",
       "4                 OK                               NaN   \n",
       "\n",
       "                                  Meta Description 1 H1-1  \\\n",
       "0                                                NaN  NaN   \n",
       "1  The official home of the Python Programming La...  NaN   \n",
       "2                                                NaN  NaN   \n",
       "3                                                NaN  NaN   \n",
       "4                                                NaN  NaN   \n",
       "\n",
       "                  H1-2  Meta Robots 1  Meta Refresh 1  \\\n",
       "0                  NaN            NaN             NaN   \n",
       "1  Django Girls Ibagué            NaN             NaN   \n",
       "2                  NaN            NaN             NaN   \n",
       "3                  NaN            NaN             NaN   \n",
       "4                  NaN            NaN             NaN   \n",
       "\n",
       "   Canonical Link Element 1  Word Count  Text Ratio  Crawl Depth  Inlinks  \\\n",
       "0                       NaN           0    0.000000            3        3   \n",
       "1                       NaN         335    6.423675            2       23   \n",
       "2                       NaN           0    0.000000            3        5   \n",
       "3                       NaN           0    0.000000            3        1   \n",
       "4                       NaN           0    0.000000            2        2   \n",
       "\n",
       "   Unique Inlinks  Outlinks  Unique Outlinks  External Outlinks  \\\n",
       "0               2         0                0                  0   \n",
       "1              15       113               45                 35   \n",
       "2               2         0                0                  0   \n",
       "3               1         0                0                  0   \n",
       "4               2         0                0                  0   \n",
       "\n",
       "   Unique External Outlinks                               Redirect URI  \n",
       "0                         0  https://www.python.org/dev/peps/pep-0470/  \n",
       "1                        22                                        NaN  \n",
       "2                         0  https://www.python.org/dev/peps/pep-0471/  \n",
       "3                         0                                        NaN  \n",
       "4                         0                                        NaN  "
      ]
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    }
   ],
   "source": [
    "# We skip the first row with `skiprows`, and\n",
    "# load only useful data with `usecols` argument.\n",
    "urls = pd.read_csv('data/internal_all.csv',skiprows=1,\n",
    "                   usecols=[\"Address\",\"Status Code\",\"Status\",\n",
    "                            \"Title 1\",\"Meta Description 1\",\"H1-1\",\"H1-2\",\"Meta Robots 1\",\n",
    "                            \"Meta Refresh 1\",\"Canonical Link Element 1\",\"Word Count\",\"Text Ratio\",\n",
    "                            \"Crawl Depth\",\"Inlinks\",\"Unique Inlinks\",\"Outlinks\",\"Unique Outlinks\",\n",
    "                            \"External Outlinks\",\"Unique External Outlinks\",\"Redirect URI\"])\n",
    "urls.head() # show the first rows of the dataframe: helpful to check if all went right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Destination</th>\n",
       "      <th>Status Code</th>\n",
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       "      <td>200.0</td>\n",
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       "      <td>AHREF</td>\n",
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       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>200.0</td>\n",
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       "      <td>200.0</td>\n",
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       "       Type                                             Source  \\\n",
       "0  REDIRECT           https://www.python.org/dev/peps/pep-0470   \n",
       "1     AHREF  https://www.python.org/events/python-user-grou...   \n",
       "2     AHREF  https://www.python.org/events/python-user-grou...   \n",
       "3     AHREF  https://www.python.org/events/python-user-grou...   \n",
       "4     AHREF  https://www.python.org/events/python-user-grou...   \n",
       "\n",
       "                                         Destination  Status Code  \n",
       "0          https://www.python.org/dev/peps/pep-0470/        200.0  \n",
       "1  https://www.python.org/events/python-user-grou...        200.0  \n",
       "2  https://www.python.org/events/python-user-grou...        200.0  \n",
       "3                            https://www.python.org/        200.0  \n",
       "4                https://www.python.org/psf-landing/        200.0  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same thing for the links\n",
    "links = pd.read_csv('data/all_outlinks.csv',skiprows=1,usecols=[\"Type\",\"Source\",\"Destination\",\"Status Code\"])\n",
    "links.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "<br/>\n",
    "## Simple grouping and charts\n",
    "\n",
    "We can now play a little with the data.  \n",
    "Let's count URLs per Status Code:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Status Code\n",
       "200    3202\n",
       "301     467\n",
       "302       3\n",
       "307     449\n",
       "404     239\n",
       "Name: Status Code, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Status Code pie chart\n",
    "urls.groupby('Status Code')['Status Code'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is nice, but ugly.  \n",
    "However, Pandas DataFrames have a very helpful [`plot()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c4e8eb8>"
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     "execution_count": 5,
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAFJCAYAAABkc8DvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAFqdJREFUeJzt3X+w5XV93/HXG1CqYhTCShFQjGIFfyHZoo6xJdXhh7WDdozxRysQnXUyEmNrJ8HEBmtiQqZNrI4GS0YCpv4co0JTohISFZOiLFH5IVLRgED5sQiiiKOg7/5xvjeerLvsZ3fv3XN39/GYuXO/5/P9nu/53J0zy3O/fO73VHcHAADYsj0WPQEAANhZiGcAABgkngEAYJB4BgCAQeIZAAAGiWcAABgkngFYVlXVVfW4Rc8DYCWIZ4DtUFXXVdX3quruqrq1qs6pqn0WPa8lU8h+d5rfN6vqoqr6xWU8/6eq6lXLdT6A1U48A2y/f9Pd+yQ5KsnaJG9c8Hw29tRpfv8syTlJ3lFVpy92SgA7J/EMsEy6+6Ykf5HkSUlSVadU1dVV9Z2q+npVvXr++Kr6taq6uar+X1W9an65Q1XtXVX/raq+MV3RfldVPWjat39V/XlVfauq7qiqi6tqi3+fd/ft3f2nSX45yRuq6qen8z2sqt49zeWmqvqdqtpz2ndyVf1NVb2jqu6qqq9U1XOmfW9J8uzMYvzuqnrH3Ms9t6q+Os3xnVVV2/nHC7AqiGeAZVJVhyR5XpIvTEO3JXl+kp9KckqSt1bVUdOxxyf5j0mem+RxSY7Z6HRnJHl8kiOn/Qcl+a1p3+uT3JhkTZIDkvxGkt6KqZ6XZK8kR0+Pz0ly3/Q6T0tybJL5pRhPT/K1JPsnOT3JR6pqv+7+zSQXJzm1u/fp7lPnnvP8JP88yVOSvDjJcVsxP4BVSzwDbL+PVdW3knw2yaeT/G6SdPf/7u6v9cynk3wysyu1ySwo/6S7r+rue5K8aelk01XadUn+Q3ff0d3fmc75kumQe5McmOTR3X1vd1/c3cPx3N33Jrk9yX5VdUBmwf+67v5ud9+W5K1zr5XM/hHw36fX+mCSa5L86y28zBnd/a3u/kaSv87sHwEAO729Fj0BgF3AC7r7LzcerKoTMrtS+/jMLlY8OMkV0+5HJlk/d/gNc9trpmMvm1vtUEn2nLb/a2ax/clp/1ndfcboZKvqAdNr3JHk0UkekOTmudfaY6P53LRRnF8/zf/+3DK3fU+SVfNLlADbQzwDrICq2jvJnyV5RZLzuvveqvpYZhGcJDcnOXjuKYfMbd+e5HtJnjito/5HpivRr0/y+qp6UpK/qqpLu/uiwemdmNkyjc8neWCS7yfZv7vv28zxB1VVzQX0o5KcvzSdwdcE2CVYtgGwMh6YZO8kG5LcN12FPnZu/4eSnFJVh1fVg5P856Ud3f2jJH+c2RrpRyRJVR1UVcdN28+vqsdNyzvuSvLDJD/a0oSqar+qenmSdyb5/e7+ZnffnNlykj+oqp+qqj2q6rFV9S/nnvqIJK+tqgdU1S8kOTzJBdO+W5P8zNb+4QDsrMQzwAqYrg6/NrNIvjPJy/Ljq7Xp7r9I8vbM1gNfm+SSadf3p++/vjReVd9O8peZ3WouSQ6bHt+d5P8k+aPu/uv7mc6Xquru6Xyvymwt9W/N7X9FZrH/5WmuH85sTfWSz02veXuStyR5UXd/c9r3tiQvqqo7q+rtW/hjAdjp1Vb8jgkAK6SqDk9yZZK972f5xA5XVScneVV3/9yi5wKwGrjyDLAgVfXC6X7O+yb5/ST/azWFMwA/STwDLM6rM7sN3NcyW7f8y4udDgBbYtkGAAAMcuUZAAAGiWcAABi0qj8kZf/99+9DDz100dMAAGAXd9lll93e3Wu2dNyqjudDDz0069ev3/KBAACwHarq+pHjLNsAAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGLTXoicAALDaXP2Ewxc9hV3G4V+5etFTWFauPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg/ba0gFVdUiS9yQ5IEknOau731ZV+yX5YJJDk1yX5MXdfWdVVZK3JXleknuSnNzdfzed66Qkb5xO/Tvdfe7y/jgA7CyefO6TFz2FXcoVJ12x6CnAbmHkyvN9SV7f3UckeUaS11TVEUlOS3JRdx+W5KLpcZKckOSw6WtdkjOTZIrt05M8PcnRSU6vqn2X8WcBAIAVtcV47u6bl64cd/d3klyd5KAkJyZZunJ8bpIXTNsnJnlPz1yS5OFVdWCS45Jc2N13dPedSS5Mcvyy/jQAALCCtmrNc1UdmuRpST6X5IDuvnnadUtmyzqSWVjfMPe0G6exzY1v/Brrqmp9Va3fsGHD1kwPAABW1HA8V9U+Sf4syeu6+9vz+7q7M1sPvd26+6zuXtvda9esWbMcpwQAgGUxFM9V9YDMwvm93f2RafjWaTlGpu+3TeM3JTlk7ukHT2ObGwcAgJ3CFuN5unvGu5Nc3d1/OLfr/CQnTdsnJTlvbvwVNfOMJHdNyzs+keTYqtp3+kXBY6cxAADYKWzxVnVJnpXk3ye5oqq+OI39RpIzknyoql6Z5PokL572XZDZbequzexWdackSXffUVW/neTS6bg3d/cdy/JTAADADrDFeO7uzyapzex+ziaO7ySv2cy5zk5y9tZMEAAAVgufMAgAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMCgLcZzVZ1dVbdV1ZVzY2+qqpuq6ovT1/Pm9r2hqq6tqmuq6ri58eOnsWur6rTl/1EAAGBljVx5PifJ8ZsYf2t3Hzl9XZAkVXVEkpckeeL0nD+qqj2ras8k70xyQpIjkrx0OhYAAHYae23pgO7+TFUdOni+E5N8oLu/n+Tvq+raJEdP+67t7q8nSVV9YDr2y1s9YwAAWJDtWfN8alVdPi3r2HcaOyjJDXPH3DiNbW78J1TVuqpaX1XrN2zYsB3TAwCA5bWt8XxmkscmOTLJzUn+YLkm1N1ndffa7l67Zs2a5TotAABsty0u29iU7r51abuq/jjJn08Pb0pyyNyhB09juZ9xAADYKWzTleeqOnDu4QuTLN2J4/wkL6mqvavqMUkOS/L5JJcmOayqHlNVD8zslwrP3/ZpAwDAjrfFK89V9f4kxyTZv6puTHJ6kmOq6sgkneS6JK9Oku6+qqo+lNkvAt6X5DXd/cPpPKcm+USSPZOc3d1XLftPAwAAK2jkbhsv3cTwu+/n+Lckecsmxi9IcsFWzQ4AAFYRnzAIAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAIPEMAACDxDMAAAwSzwAAMEg8AwDAoC3Gc1WdXVW3VdWVc2P7VdWFVfXV6fu+03hV1dur6tqquryqjpp7zknT8V+tqpNW5scBAICVM3Ll+Zwkx280dlqSi7r7sCQXTY+T5IQkh01f65KcmcxiO8npSZ6e5Ogkpy8FNwAA7Cy2GM/d/Zkkd2w0fGKSc6ftc5O8YG78PT1zSZKHV9WBSY5LcmF339Hddya5MD8Z5AAAsKpt65rnA7r75mn7liQHTNsHJblh7rgbp7HNjf+EqlpXVeurav2GDRu2cXoAALD8tvsXBru7k/QyzGXpfGd199ruXrtmzZrlOi0AAGy3bY3nW6flGJm+3zaN35TkkLnjDp7GNjcOAAA7jW2N5/OTLN0x46Qk582Nv2K668Yzktw1Le/4RJJjq2rf6RcFj53GAABgp7HXlg6oqvcnOSbJ/lV1Y2Z3zTgjyYeq6pVJrk/y4unwC5I8L8m1Se5JckqSdPcdVfXbSS6djntzd2/8S4gAALCqbTGeu/ulm9n1nE0c20les5nznJ3k7K2aHQAArCI+YRAAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB4hkAAAaJZwAAGCSeAQBgkHgGAIBB2xXPVXVdVV1RVV+sqvXT2H5VdWFVfXX6vu80XlX19qq6tqour6qjluMHAACAHWU5rjz/fHcf2d1rp8enJbmouw9LctH0OElOSHLY9LUuyZnL8NoAALDDrMSyjROTnDttn5vkBXPj7+mZS5I8vKoOXIHXBwCAFbG98dxJPllVl1XVumnsgO6+edq+JckB0/ZBSW6Ye+6N09g/UlXrqmp9Va3fsGHDdk4PAACWz17b+fyf6+6bquoRSS6sqq/M7+zurqremhN291lJzkqStWvXbtVzAQBgJW3Xlefuvmn6fluSjyY5OsmtS8sxpu+3TYfflOSQuacfPI0BAMBOYZvjuaoeUlUPXdpOcmySK5Ocn+Sk6bCTkpw3bZ+f5BXTXTeekeSuueUdAACw6m3Pso0Dkny0qpbO877u/nhVXZrkQ1X1yiTXJ3nxdPwFSZ6X5Nok9yQ5ZTteGwAAdrhtjufu/nqSp25i/JtJnrOJ8U7ymm19PQAAWDSfMAgAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIPEMwAADBLPAAAwSDwDAMAg8QwAAIP2WvQEgBX2poctega7jjfdtegZALBgrjwDAMAg8QwAAIN2eDxX1fFVdU1VXVtVp+3o1wcAgG21Q+O5qvZM8s4kJyQ5IslLq+qIHTkHAADYVjv6yvPRSa7t7q939w+SfCDJiTt4DgAAsE129N02Dkpyw9zjG5M8ff6AqlqXZN308O6qumYHzW13sH+S2xc9CdiM1f/+/C+16BmwGKv/vZmkTvb+3A3tFO/N1E7z3nz0yEGr7lZ13X1WkrMWPY9dUVWt7+61i54HbIr3J6uV9yarlffmYuzoZRs3JTlk7vHB0xgAAKx6OzqeL01yWFU9pqoemOQlSc7fwXMAAIBtskOXbXT3fVV1apJPJNkzydndfdWOnMNuznIYVjPvT1Yr701WK+/NBajuXvQcAABgp+ATBgEAYJB4BgCAQeIZAAAGrbr7PLN8quoJmX2C40HT0E1Jzu/uqxc3K4DVbfq786Akn+vuu+fGj+/ujy9uZuzuquroJN3dl1bVEUmOT/KV7r5gwVPbrbjyvIuqql/P7OPPK8nnp69K8v6qOm2Rc4P7U1WnLHoO7L6q6rVJzkvyK0murKoT53b/7mJmBUlVnZ7k7UnOrKrfS/KOJA9JclpV/eZCJ7ebcbeNXVRV/d8kT+zuezcaf2CSq7r7sMXMDO5fVX2jux+16Hmwe6qqK5I8s7vvrqpDk3w4yZ9299uq6gvd/bSFTpDd1vTePDLJ3kluSXJwd3+7qh6U2f8lecpCJ7gbsWxj1/WjJI9Mcv1G4wdO+2Bhquryze1KcsCOnAtsZI+lpRrdfV1VHZPkw1X16Mzen7Ao93X3D5PcU1Vf6+5vJ0l3f6+q/Hd9BxLPu67XJbmoqr6a5IZp7FFJHpfk1IXNCmYOSHJckjs3Gq8kf7vjpwP/4NaqOrK7v5gk0xXo5yc5O8mTFzs1dnM/qKoHd/c9SX52abCqHhYXxXYoyzZ2YVW1R5Kj849/YfDS6V+usDBV9e4kf9Ldn93Evvd198sWMC1IVR2c2RW+Wzax71nd/TcLmBakqvbu7u9vYnz/JAd29xULmNZuSTwDAMAgd9sAAIBB4hkAAAaJZ4AVUlX/tKo+UFVfq6rLquqCqnr8Mr/Gp6pq7WbGr6mqy6vqK1X1jqp6+Ha8zslV9ci5x9dNay0BdiviGWAFVFUl+WiST3X3Y7v7Z5O8IRvdiq+qVvKuRy+f7v36lCTfz+zDP7bVyZnd/hJgtyaeAVbGzye5t7vftTTQ3V/q7our6piquriqzk/y5SSpqo9NV6evqqp109gvVNUfTtu/WlVfn7Z/pqqG7/rQ3T9I8mtJHlVVT53O8e+q6vNV9cWq+h9Vtec0fndVvXWax0VVtaaqXpRkbZL3Tsc/aDr1r1TV31XVFdNHWgPs8sQzwMp4UpLL7mf/UUl+tbuXlnH80nR1em2S11bVTye5OMmzp/3PTvLNqjpo2v7M1kxmukXll5I8oaoOT/KLSZ7V3Ucm+WGSl0+HPiTJ+u5+YpJPJzm9uz+cZH1mV7KP7O7vTcfe3t1HJTkzyX/amvkA7Kx8SArAYny+u/9+7vFrq+qF0/YhSQ7r7kuqap+qeug09r4k/yKzeP7INrzm0ifkPSezD1m4dLa6JA9Kctu070dJPjht/88tvM7SvsuS/NttmA/ATkc8A6yMq5K86H72f3dpY/oI6OcmeWZ331NVn0ryT6bdf5vklCTXZHYl+peSPDPJ67dmMtOyjCcnuTrJI5Kc291vGHjq/X0YwNIHNvww/nsC7CYs2wBYGX+VZO+l9ctJUlVPqapnb+LYhyW5cwrnJyR5xty+izNbEvGZJF/IbC3197v7rtGJVNUDkvxekhu6+/IkFyV5UVU9Ytq/X1U9ejp8j/w4+l+WZOlTIL+T5KGjrwmwqxLPACugZx/f+sIkz51uVXdVZgH7Ex/7nOTjSfaqqquTnJHkkrl9F2e2ZOMz07rlG/LjoN2S91bV5UmuzGwt84nT3L6c5I1JPjntvzDJgdNzvpvk6Kq6Msm/SvLmafycJO/a6BcGAXY7Pp4bgH9QVXd39z6LngfAauXKMwAADHLlGQAABrnyDAAAg8QzAAAMEs8AADBIPAMAwCDxDAAAg8QzAAAM+v+pshYVkoeevAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cbe8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Depth bar chart\n",
    "urls.groupby('Crawl Depth')['Crawl Depth'].count().plot(kind=\"bar\", figsize=(12,5), title='Pages Depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might want to save this kind of chart to a file. Here's how:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5459e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = urls.groupby(['Crawl Depth','Status Code'])['Crawl Depth'].count().unstack().plot(kind=\"bar\", figsize=(12,5), title='Status Codes per Depth', stacked=True)\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('output/pic.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "<br/>\n",
    "## Filtering and exporting\n",
    "Using some built-in filter functionnalities we can access to specific data.  \n",
    "Example: filtering `links` to find any external link.  \n",
    "`~` is the \"not\" symbol for filters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Type</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Status Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://python.org/dev/peps/</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://python.org/dev/peps/</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://docs.python.org/</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://pypi.python.org/</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://plus.google.com/+Python</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://www.facebook.com/pythonlang?fref=ts</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://twitter.com/ThePSF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://brochure.getpython.info/</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://docs.python.org/3/license.html</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://wiki.python.org/moin/BeginnersGuide</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Type                                             Source  \\\n",
       "31   AHREF  https://www.python.org/events/python-user-grou...   \n",
       "77   AHREF  https://www.python.org/events/python-user-grou...   \n",
       "114  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "115  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "116  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "117  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "118  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "119  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "120  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "121  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "\n",
       "                                     Destination  Status Code  \n",
       "31                   http://python.org/dev/peps/        301.0  \n",
       "77                   http://python.org/dev/peps/        301.0  \n",
       "114                     https://docs.python.org/          NaN  \n",
       "115                     https://pypi.python.org/          NaN  \n",
       "116               http://plus.google.com/+Python          NaN  \n",
       "117   http://www.facebook.com/pythonlang?fref=ts          NaN  \n",
       "118                    http://twitter.com/ThePSF          NaN  \n",
       "119              http://brochure.getpython.info/          NaN  \n",
       "120       https://docs.python.org/3/license.html          NaN  \n",
       "121  https://wiki.python.org/moin/BeginnersGuide          NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links[~links['Destination'].str.contains('^https://www.python.org')].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `to_csv()` function exports data to CSV files on the go.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export broken links to CSV\n",
    "links[links[\"Status Code\"] == 404].to_csv('output/404-links.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also try more complex filters. For example, let's select HTTP 200 URLs without a Title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Status Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Title 1</th>\n",
       "      <th>Meta Description 1</th>\n",
       "      <th>H1-1</th>\n",
       "      <th>H1-2</th>\n",
       "      <th>Meta Robots 1</th>\n",
       "      <th>Meta Refresh 1</th>\n",
       "      <th>Canonical Link Element 1</th>\n",
       "      <th>Word Count</th>\n",
       "      <th>Text Ratio</th>\n",
       "      <th>Crawl Depth</th>\n",
       "      <th>Inlinks</th>\n",
       "      <th>Unique Inlinks</th>\n",
       "      <th>Outlinks</th>\n",
       "      <th>Unique Outlinks</th>\n",
       "      <th>External Outlinks</th>\n",
       "      <th>Unique External Outlinks</th>\n",
       "      <th>Redirect URI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.python.org/m/files/f990-2001.pdf</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.python.org/ftp/python/3.5.0/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>https://www.python.org/ftp/python/3.6.1/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>https://www.python.org/ftp/python/3.4.4/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>https://www.python.org/ftp/python/2.1.3/Python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>https://www.python.org/ftp/python/3.6.0/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>https://www.python.org/ftp/python/3.7.0/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>https://www.python.org/ftp/python/2.7.2/Python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>https://www.python.org/ftp/python/2.7.13/pytho...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>https://www.python.org/ftp/python/3.4.2/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Address  Status Code Status  \\\n",
       "3        https://www.python.org/m/files/f990-2001.pdf          200     OK   \n",
       "4   https://www.python.org/ftp/python/3.5.0/python...          200     OK   \n",
       "7   https://www.python.org/ftp/python/3.6.1/python...          200     OK   \n",
       "10  https://www.python.org/ftp/python/3.4.4/python...          200     OK   \n",
       "13  https://www.python.org/ftp/python/2.1.3/Python...          200     OK   \n",
       "14  https://www.python.org/ftp/python/3.6.0/python...          200     OK   \n",
       "15  https://www.python.org/ftp/python/3.7.0/python...          200     OK   \n",
       "17  https://www.python.org/ftp/python/2.7.2/Python...          200     OK   \n",
       "19  https://www.python.org/ftp/python/2.7.13/pytho...          200     OK   \n",
       "22  https://www.python.org/ftp/python/3.4.2/python...          200     OK   \n",
       "\n",
       "   Title 1 Meta Description 1 H1-1 H1-2  Meta Robots 1  Meta Refresh 1  \\\n",
       "3      NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "4      NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "7      NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "10     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "13     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "14     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "15     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "17     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "19     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "22     NaN                NaN  NaN  NaN            NaN             NaN   \n",
       "\n",
       "    Canonical Link Element 1  Word Count  Text Ratio  Crawl Depth  Inlinks  \\\n",
       "3                        NaN           0         0.0            3        1   \n",
       "4                        NaN           0         0.0            2        2   \n",
       "7                        NaN           0         0.0            2        2   \n",
       "10                       NaN           0         0.0            2        2   \n",
       "13                       NaN           0         0.0            2        3   \n",
       "14                       NaN           0         0.0            2        2   \n",
       "15                       NaN           0         0.0            2        2   \n",
       "17                       NaN           0         0.0            2        2   \n",
       "19                       NaN           0         0.0            3        1   \n",
       "22                       NaN           0         0.0            2        2   \n",
       "\n",
       "    Unique Inlinks  Outlinks  Unique Outlinks  External Outlinks  \\\n",
       "3                1         0                0                  0   \n",
       "4                2         0                0                  0   \n",
       "7                2         0                0                  0   \n",
       "10               2         0                0                  0   \n",
       "13               3         0                0                  0   \n",
       "14               2         0                0                  0   \n",
       "15               2         0                0                  0   \n",
       "17               2         0                0                  0   \n",
       "19               1         0                0                  0   \n",
       "22               2         0                0                  0   \n",
       "\n",
       "    Unique External Outlinks Redirect URI  \n",
       "3                          0          NaN  \n",
       "4                          0          NaN  \n",
       "7                          0          NaN  \n",
       "10                         0          NaN  \n",
       "13                         0          NaN  \n",
       "14                         0          NaN  \n",
       "15                         0          NaN  \n",
       "17                         0          NaN  \n",
       "19                         0          NaN  \n",
       "22                         0          NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urls[(urls[\"Status Code\"] == 200) & (urls[\"Title 1\"].isnull())].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## Data enhancement\n",
    "\n",
    "Several Pandas DataFrames can be merged together, which is very helpful when you want to add external data to enhance your analysis.\n",
    "\n",
    "For example, we can use our [categorize script](https://gitlab.com/databulle/categorize) to associate each crawled URL with its category.  \n",
    "\n",
    "__Step 1:__ save URLs to a new file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save URLs to file\n",
    "urls['Address'].to_csv('data/urls.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Step 2:__ use the script to categorize URLs, and save results as `categorized.csv`.  \n",
    "See [our blog](https://www.databulle.com/blog/code/categorize.html) for more details on how to use this script.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Step 3:__ load categorized URLs into our DataFrame.  \n",
    "\n",
    "We'll create a new DataFrame (`cats`) in which we'll load our categorized URLs.  \n",
    "Then, using the `merge()` function we'll add this newly generated data to our original `urls` DataFrame.  \n",
    "`merge()` works a lot like joins in SQL, but you'll find more on that in [the documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Status Code</th>\n",
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       "      <th>Title 1</th>\n",
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       "      <th>Meta Refresh 1</th>\n",
       "      <th>Canonical Link Element 1</th>\n",
       "      <th>...</th>\n",
       "      <th>Text Ratio</th>\n",
       "      <th>Crawl Depth</th>\n",
       "      <th>Inlinks</th>\n",
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       "      <th>Outlinks</th>\n",
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       "      <th>Redirect URI</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.python.org/dev/peps/pep-0470</td>\n",
       "      <td>301</td>\n",
       "      <td>MOVED PERMANENTLY</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>https://www.python.org/dev/peps/pep-0470/</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>Django Girls Ibagué | Python.org</td>\n",
       "      <td>The official home of the Python Programming La...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Django Girls Ibagué</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.423675</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>113</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>events</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.python.org/dev/peps/pep-0471</td>\n",
       "      <td>301</td>\n",
       "      <td>MOVED PERMANENTLY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.python.org/dev/peps/pep-0471/</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.python.org/m/files/f990-2001.pdf</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>files</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.python.org/ftp/python/3.5.0/python...</td>\n",
       "      <td>200</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ftp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Address  Status Code  \\\n",
       "0           https://www.python.org/dev/peps/pep-0470          301   \n",
       "1  https://www.python.org/events/python-user-grou...          200   \n",
       "2           https://www.python.org/dev/peps/pep-0471          301   \n",
       "3       https://www.python.org/m/files/f990-2001.pdf          200   \n",
       "4  https://www.python.org/ftp/python/3.5.0/python...          200   \n",
       "\n",
       "              Status                           Title 1  \\\n",
       "0  MOVED PERMANENTLY                               NaN   \n",
       "1                 OK  Django Girls Ibagué | Python.org   \n",
       "2  MOVED PERMANENTLY                               NaN   \n",
       "3                 OK                               NaN   \n",
       "4                 OK                               NaN   \n",
       "\n",
       "                                  Meta Description 1 H1-1  \\\n",
       "0                                                NaN  NaN   \n",
       "1  The official home of the Python Programming La...  NaN   \n",
       "2                                                NaN  NaN   \n",
       "3                                                NaN  NaN   \n",
       "4                                                NaN  NaN   \n",
       "\n",
       "                  H1-2  Meta Robots 1  Meta Refresh 1  \\\n",
       "0                  NaN            NaN             NaN   \n",
       "1  Django Girls Ibagué            NaN             NaN   \n",
       "2                  NaN            NaN             NaN   \n",
       "3                  NaN            NaN             NaN   \n",
       "4                  NaN            NaN             NaN   \n",
       "\n",
       "   Canonical Link Element 1   ...     Text Ratio  Crawl Depth  Inlinks  \\\n",
       "0                       NaN   ...       0.000000            3        3   \n",
       "1                       NaN   ...       6.423675            2       23   \n",
       "2                       NaN   ...       0.000000            3        5   \n",
       "3                       NaN   ...       0.000000            3        1   \n",
       "4                       NaN   ...       0.000000            2        2   \n",
       "\n",
       "   Unique Inlinks  Outlinks  Unique Outlinks  External Outlinks  \\\n",
       "0               2         0                0                  0   \n",
       "1              15       113               45                 35   \n",
       "2               2         0                0                  0   \n",
       "3               1         0                0                  0   \n",
       "4               2         0                0                  0   \n",
       "\n",
       "   Unique External Outlinks                               Redirect URI  \\\n",
       "0                         0  https://www.python.org/dev/peps/pep-0470/   \n",
       "1                        22                                        NaN   \n",
       "2                         0  https://www.python.org/dev/peps/pep-0471/   \n",
       "3                         0                                        NaN   \n",
       "4                         0                                        NaN   \n",
       "\n",
       "  Category  \n",
       "0      dev  \n",
       "1   events  \n",
       "2      dev  \n",
       "3    files  \n",
       "4      ftp  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load categorized URLs\n",
    "cats = pd.read_csv('data/categorized.csv')\n",
    "cats.columns = ['Address','Category']\n",
    "# Merge the two DataFrames\n",
    "urls = urls.merge(cats, on='Address', how='left')\n",
    "urls.head()\n",
    "# Check to the right for your new `Category` column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this new data is very easy. Here are some example charts:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c549470>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f456588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categories pie chart\n",
    "urls.groupby('Category')['Category'].count().plot(kind=\"pie\", figsize=(8,8), title='Categories')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c238ef0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c576a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categories / Depth\n",
    "urls.groupby([\"Crawl Depth\",\"Category\"])['Address'].count().unstack().plot(kind=\"bar\", figsize=(12,7), title='Categories / Depth', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11007ce80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5c4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlinks per category\n",
    "urls.groupby(\"Category\")[\"Outlinks\"].sum().plot(kind=\"barh\", figsize=(12,7), title=\"Outlinks per category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1101142b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11011cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Words per category\n",
    "urls.groupby(\"Category\")[\"Word Count\"].mean().plot(kind=\"barh\", figsize=(12,7), title=\"Words per category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could also try some more complex merging. For instance, this finds all links to 301 redirects and adds redirect URL to the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Type</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Status Code</th>\n",
       "      <th>Redirect URI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://www.python.org/download/alternatives</td>\n",
       "      <td>301.0</td>\n",
       "      <td>https://www.python.org/download/alternatives/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://www.python.org/doc/av</td>\n",
       "      <td>301.0</td>\n",
       "      <td>https://www.python.org/doc/av/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>http://python.org/dev/peps/</td>\n",
       "      <td>301.0</td>\n",
       "      <td>https://python.org/dev/peps/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://www.python.org/community/awards</td>\n",
       "      <td>301.0</td>\n",
       "      <td>https://www.python.org/community/awards/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHREF</td>\n",
       "      <td>https://www.python.org/events/python-user-grou...</td>\n",
       "      <td>https://www.python.org/download/alternatives</td>\n",
       "      <td>301.0</td>\n",
       "      <td>https://www.python.org/download/alternatives/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Type                                             Source  \\\n",
       "0  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "1  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "2  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "3  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "4  AHREF  https://www.python.org/events/python-user-grou...   \n",
       "\n",
       "                                    Destination  Status Code  \\\n",
       "0  https://www.python.org/download/alternatives        301.0   \n",
       "1                 https://www.python.org/doc/av        301.0   \n",
       "2                   http://python.org/dev/peps/        301.0   \n",
       "3       https://www.python.org/community/awards        301.0   \n",
       "4  https://www.python.org/download/alternatives        301.0   \n",
       "\n",
       "                                    Redirect URI  \n",
       "0  https://www.python.org/download/alternatives/  \n",
       "1                 https://www.python.org/doc/av/  \n",
       "2                   https://python.org/dev/peps/  \n",
       "3       https://www.python.org/community/awards/  \n",
       "4  https://www.python.org/download/alternatives/  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new DataFrame with 301 links\n",
    "redirs = links[links[\"Status Code\"] == 301]\n",
    "# Merge with `urls` to get Redirect URI\n",
    "redirs = redirs.merge(urls[urls[\"Status Code\"] == 301][[\"Address\",\"Redirect URI\"]], how=\"left\", left_on=\"Destination\", right_on=\"Address\")\n",
    "# Drop Address column\n",
    "redirs = redirs.drop(\"Address\", axis=1)\n",
    "# Show first 10 rows\n",
    "redirs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What now ?\n",
    "\n",
    "Those were just a few basic examples of what you can do with a few lines of code.  \n",
    "You could add data from APIs, try some Machine Learning libs, calculate internal PageRank... It's up to you now!  "
   ]
  }
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