hcds-a1-data-curation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 0: SETUP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below libraries are used for this project. For a full list of requirements and versions, please see the requirements.txt file included in the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 1: DATA ACQUISITION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "Data is acquired through the Wikimedia REST API and saved as json files. These files are included in the repository in the *data* folder; you made skip to Stage 2 and use the included files if desired, or skip to Stage 3 to skip processing entirely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will request data from both the [Legacy](https://wikitech.wikimedia.org/wiki/Analytics/AQS/Legacy_Pagecounts) and [Pageviews](https://wikitech.wikimedia.org/wiki/Analytics/AQS/Pageviews) API.\n",
    "\n",
    "We define base templates for the parameters. English wikipedia with monthyl granularity will always be requested, and on the pageviews api we always request agent=user to filter out crawler and bot traffic. We also request consistent dateranges for each api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint_legacy = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{site}/{granularity}/{start}/{end}'\n",
    "endpoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{site}/{agent}/{granularity}/{start}/{end}'\n",
    "params_legacy = {\"project\" : \"en.wikipedia.org\",\n",
    "                 \"granularity\" : \"monthly\",\n",
    "                 \"start\" : \"2008010100\",\n",
    "                 \"end\" : \"2016080100\"\n",
    "                }\n",
    "\n",
    "params_pageviews = {\"project\" : \"en.wikipedia.org\",\n",
    "                    \"agent\" : \"user\",\n",
    "                    \"granularity\" : \"monthly\",\n",
    "                    \"start\" : \"2015070100\",\n",
    "                    \"end\" : \"2021090100\"\n",
    "                }\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We request each endpoint for each access type, except for aggregates. All data is saved in the *data* folder.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def api_call(endpoint,parameters):\n",
    "    headers = {\n",
    "        'User-Agent': 'https://github.com/Cain93',\n",
    "        'From': 'ccase20@uw.edu'\n",
    "    }\n",
    "    call = requests.get(endpoint.format(**parameters), headers=headers)\n",
    "    response = call.json()\n",
    "    \n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "legacy_sites = [\"desktop-site\", \"mobile-site\"]\n",
    "pageview_sites = [\"desktop\", \"mobile-app\", \"mobile-web\"]\n",
    "file_template = \"data/{apiname}_{site}_{daterange}.json\"\n",
    "for site in legacy_sites:\n",
    "    data = api_call(endpoint_legacy, {**params_legacy, \"site\":site})\n",
    "    fileName = file_template.format(apiname=\"pagecount\", site=site, daterange = \"200801-201607\")\n",
    "    with open(fileName, 'w') as outfile:\n",
    "        json.dump(data, outfile)\n",
    "for site in pageview_sites:\n",
    "    data = api_call(endpoint_pageviews, {**params_pageviews, \"site\":site})\n",
    "    fileName = file_template.format(apiname=\"pageview\", site=site, daterange = \"201507-202108\")\n",
    "    with open(fileName, 'w') as outfile:\n",
    "        json.dump(data, outfile)  \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 2: DATA PROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is consoldiated and formatted into a single file. This file is included in the repository as *en-wikipedia_traffic_200712-202108.csv*; you may skip to Stage 3 and use the included file if desired.\n",
    "\n",
    "First we open each file and combine into a dataframe. While doing, we rename columns to make them consistent between legacy and pageview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>project</th>\n",
       "      <th>access</th>\n",
       "      <th>granularity</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>views</th>\n",
       "      <th>agent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>en.wikipedia</td>\n",
       "      <td>desktop-site</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2008010100</td>\n",
       "      <td>4930902570</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>en.wikipedia</td>\n",
       "      <td>desktop-site</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2008020100</td>\n",
       "      <td>4818393763</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>en.wikipedia</td>\n",
       "      <td>desktop-site</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2008030100</td>\n",
       "      <td>4955405809</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>en.wikipedia</td>\n",
       "      <td>desktop-site</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2008040100</td>\n",
       "      <td>5159162183</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>en.wikipedia</td>\n",
       "      <td>desktop-site</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2008050100</td>\n",
       "      <td>5584691092</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        project        access granularity   timestamp       views agent\n",
       "0  en.wikipedia  desktop-site     monthly  2008010100  4930902570   NaN\n",
       "1  en.wikipedia  desktop-site     monthly  2008020100  4818393763   NaN\n",
       "2  en.wikipedia  desktop-site     monthly  2008030100  4955405809   NaN\n",
       "3  en.wikipedia  desktop-site     monthly  2008040100  5159162183   NaN\n",
       "4  en.wikipedia  desktop-site     monthly  2008050100  5584691092   NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = pd.DataFrame()\n",
    "col_names = {\n",
    "    \"access-site\": \"access\",\n",
    "    \"count\": \"views\"\n",
    "}\n",
    "\n",
    "for filename in os.listdir(\"data\"):\n",
    "    file = open(\"data/\" + filename, \"r\")\n",
    "    file_data = json.loads(file.read())\n",
    "    file_df = pd.DataFrame.from_records(file_data[\"items\"]).rename(columns = col_names)\n",
    "    \n",
    "    combined_data = combined_data.append(file_df)\n",
    "\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we parse the timestamp into year and month, and remove unused columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>access</th>\n",
       "      <th>views</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>desktop-site</td>\n",
       "      <td>4930902570</td>\n",
       "      <td>2008</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>desktop-site</td>\n",
       "      <td>4818393763</td>\n",
       "      <td>2008</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>desktop-site</td>\n",
       "      <td>4955405809</td>\n",
       "      <td>2008</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>desktop-site</td>\n",
       "      <td>5159162183</td>\n",
       "      <td>2008</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>desktop-site</td>\n",
       "      <td>5584691092</td>\n",
       "      <td>2008</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         access       views  year month\n",
       "0  desktop-site  4930902570  2008    01\n",
       "1  desktop-site  4818393763  2008    02\n",
       "2  desktop-site  4955405809  2008    03\n",
       "3  desktop-site  5159162183  2008    04\n",
       "4  desktop-site  5584691092  2008    05"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data[\"year\"] = combined_data[\"timestamp\"].apply(lambda x: x[0:4])\n",
    "combined_data[\"month\"] = combined_data[\"timestamp\"].apply(lambda x: x[4:6])\n",
    "cleaned_data = combined_data.drop(columns=[\"timestamp\", \"granularity\", \"project\", \"agent\"])\n",
    "cleaned_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now data is pivoted to create a new column for each type of view. After pivoting:\n",
    "1. Mobile-web and mobile-app columns are combined into mobile\n",
    "1. Columns are rename into more descriptive names\n",
    "1. Aggregate columns for all pageview and pagecount views are created\n",
    "1. Unused columns are dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "access      desktop  desktop-site  mobile-app  mobile-site  mobile-web\n",
      "year month                                                            \n",
      "2008 01         0.0  4.930903e+09         0.0          0.0         0.0\n",
      "     02         0.0  4.818394e+09         0.0          0.0         0.0\n",
      "     03         0.0  4.955406e+09         0.0          0.0         0.0\n",
      "     04         0.0  5.159162e+09         0.0          0.0         0.0\n",
      "     05         0.0  5.584691e+09         0.0          0.0         0.0\n"
     ]
    },
    {
     "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>access</th>\n",
       "      <th>pageview_desktop_views</th>\n",
       "      <th>pagecount_desktop_views</th>\n",
       "      <th>pagecount_mobile_views</th>\n",
       "      <th>pageview_mobile_views</th>\n",
       "      <th>pagecount_all_views</th>\n",
       "      <th>pageview_all_views</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2008</th>\n",
       "      <th>01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.930903e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.930903e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.818394e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.818394e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.955406e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.955406e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.159162e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.159162e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.584691e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.584691e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "access      pageview_desktop_views  pagecount_desktop_views  \\\n",
       "year month                                                    \n",
       "2008 01                        0.0             4.930903e+09   \n",
       "     02                        0.0             4.818394e+09   \n",
       "     03                        0.0             4.955406e+09   \n",
       "     04                        0.0             5.159162e+09   \n",
       "     05                        0.0             5.584691e+09   \n",
       "\n",
       "access      pagecount_mobile_views  pageview_mobile_views  \\\n",
       "year month                                                  \n",
       "2008 01                        0.0                    0.0   \n",
       "     02                        0.0                    0.0   \n",
       "     03                        0.0                    0.0   \n",
       "     04                        0.0                    0.0   \n",
       "     05                        0.0                    0.0   \n",
       "\n",
       "access      pagecount_all_views  pageview_all_views  \n",
       "year month                                           \n",
       "2008 01            4.930903e+09                 0.0  \n",
       "     02            4.818394e+09                 0.0  \n",
       "     03            4.955406e+09                 0.0  \n",
       "     04            5.159162e+09                 0.0  \n",
       "     05            5.584691e+09                 0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot\n",
    "pivot_data = cleaned_data.pivot(index = [\"year\", \"month\"], columns=[\"access\"])\n",
    "pivot_data.columns = pivot_data.columns.droplevel()\n",
    "\n",
    "# Replace NaN with 0\n",
    "pivot_data = pivot_data.fillna(0)\n",
    "\n",
    "print(pivot_data.head())\n",
    "\n",
    "# Combine mobil views\n",
    "pivot_data[\"mobile\"] = pivot_data[\"mobile-web\"] + pivot_data[\"mobile-app\"]\n",
    "pivot_data = pivot_data.drop(columns = [\"mobile-web\", \"mobile-app\"])\n",
    "\n",
    "# Rename and aggregate\n",
    "pivot_data = pivot_data.rename(columns = {\"desktop-site\":\"pagecount_desktop_views\",\n",
    "                                          \"mobile-site\": \"pagecount_mobile_views\",\n",
    "                                          \"desktop\":\"pageview_desktop_views\",\n",
    "                                          \"mobile\":\"pageview_mobile_views\",\n",
    "                                          })\n",
    "pivot_data[\"pagecount_all_views\"] = pivot_data[\"pagecount_desktop_views\"] + pivot_data[\"pagecount_mobile_views\"]\n",
    "pivot_data[\"pageview_all_views\"] = pivot_data[\"pageview_desktop_views\"] + pivot_data[\"pageview_mobile_views\"]\n",
    "\n",
    "pivot_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is converted to csv and saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_data.to_csv('en-wikipedia_traffic_200712-202108.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 3: ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some minor processing is necessary to dispaly. The year and month are combined into a datetime index, and 0's are replaced with Nan to avoid plotting them. Values re-scaled to be in billions for easier interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "display_data = pd.read_csv('en-wikipedia_traffic_200712-202108.csv')\n",
    "\n",
    "# Set the index as dates\n",
    "display_data.index = pd.to_datetime(display_data[['year', 'month']].assign(DAY=1))\n",
    "display_data.drop(columns=[\"year\", \"month\"], inplace=True)\n",
    "\n",
    "# Repplce 0 with Nan\n",
    "display_data = display_data.replace(0, np.nan)\n",
    "\n",
    "# Rescale to billions\n",
    "display_data = display_data / 1000000000\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With some minor formatting, we see the trend in views. In 2016 we can see the difference in the new pageview API totals compared to the legacy, attributable to the amount of web crawlers accessing pages. \n",
    "\n",
    "Also visible is the growth of mobile access from when it was first recorded in late 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Desktop\", \"Desktop (Legacy)\", \"Mobile (Legacy)\", \"Mobile\", \"Total (Legacy)\", \"Total\"]\n",
    "styles = [\"g--\",\"b--\", \"b:\", \"g:\", \"b\", \"g\"]\n",
    "widths = [3,3,3,3,6,6]\n",
    "\n",
    "ax = display_data.plot(figsize=(15, 6), style=styles, title=\"English Wikipedia Traffic\")\n",
    "\n",
    "# Reorder legend\n",
    "handles = plt.gca().get_legend_handles_labels()[0]\n",
    "order = [5,0,3,4,1,2]\n",
    "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])\n",
    "\n",
    "# Axis titles\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Views in Billions\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save plot\n",
    "ax.get_figure().savefig(\"english_wikipedia_traffic.png\")"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}