example/example_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `gtab` example usage\n",
    "When querying Google Trends, it outputs scores as integers in the range $[0, 100]$.\n",
    "For example, when we query Facebook (/m/02y1vz), Twitter (/m/0289n8t), and EPFL (/m/0jg7r, a swiss university) we get the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Image\n",
    "display(Image(filename='./imgs/fb_tw_ep.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two problems here:\n",
    "\n",
    "1. EPFL is always listed as  $<1\\%$, so it does not convey any information to us!\n",
    "\n",
    "2. GTrends limits us to 5 keywords! Would not work if we wanted to compare dozens or hundreds of keywords?\n",
    "\n",
    "To solve both of these issues we can use the `gtab`! \n",
    "\n",
    "## 1: Setting it up!\n",
    "We need to create a `GTAB` object with the desired path. This will create and initialize the directory structure of `my_path` if it does not exist. If it does, it assumes we have already set up that directory for use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gtab in /Users/horta/opt/anaconda3/envs/GoogleTrendsAnchorBank/lib/python3.7/site-packages (0.1)\n",
      "Directory already exists, loading data from it.\n",
      "Using directory 'example_path'\n",
      "Active anchorbank changed to: google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!pip install gtab\n",
    "import gtab\n",
    "my_path = \"example_path\"\n",
    "t = gtab.GTAB(dir_path=my_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two default anchor banks included in the package. To list them use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "t.list_gtabs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`google_anchorbank_geo=_` is the global anchor bank. \n",
    "\n",
    "In this example we will continue to use it, but you could change to another anchor bank, for example the Italian one by using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active anchorbank changed to: google_anchorbank_geo=IT_timeframe=2019-01-01 2020-08-01.tsv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "t.set_active_gtab(\"google_anchorbank_geo=IT_timeframe=2019-01-01 2020-08-01.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reverts to the global anchor bank:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active anchorbank changed to: google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "t.set_active_gtab(\"google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2: Making a query! \n",
    "Now we can request a new query and calibrate it using our selected anchor bank! \n",
    "\n",
    "The `new_query` method will output a dataframe containing, as index the dates, and as columns the estimated value `max_ratio`, and a error interval related to our method `max_ratio_hi` and `max_ratio_lo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using example_path/output/google_anchorbanks/google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n",
      "New query '/m/0jg7r'\n",
      "New query calibrated!\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>max_ratio</th>\n",
       "      <th>max_ratio_hi</th>\n",
       "      <th>max_ratio_lo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.328232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.328232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.328232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.311289</td>\n",
       "      <td>0.289040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.341907</td>\n",
       "      <td>0.318434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.260258</td>\n",
       "      <td>0.240051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-05</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.260258</td>\n",
       "      <td>0.240051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.270464</td>\n",
       "      <td>0.249848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-19</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.260258</td>\n",
       "      <td>0.240051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.270464</td>\n",
       "      <td>0.249848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_ratio  max_ratio_hi  max_ratio_lo\n",
       "date                                             \n",
       "2019-01-06       0.34      0.352113      0.328232\n",
       "2019-01-13       0.34      0.352113      0.328232\n",
       "2019-01-20       0.34      0.352113      0.328232\n",
       "2019-01-27       0.30      0.311289      0.289040\n",
       "2019-02-03       0.33      0.341907      0.318434\n",
       "...               ...           ...           ...\n",
       "2020-06-28       0.25      0.260258      0.240051\n",
       "2020-07-05       0.25      0.260258      0.240051\n",
       "2020-07-12       0.26      0.270464      0.249848\n",
       "2020-07-19       0.25      0.260258      0.240051\n",
       "2020-07-26       0.26      0.270464      0.249848\n",
       "\n",
       "[82 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid = \"/m/0jg7r\" # freebase code for EPFL\n",
    "nq_res= t.new_query(mid) \n",
    "nq_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3: Creating your own anchor bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already exists, loading data from it.\n",
      "Using directory 'example_path'\n",
      "Active anchorbank changed to: google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "my_path = \"example_path\"\n",
    "t = gtab.GTAB(dir_path=my_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, if we want to construct an anchorbank with data from Germany between March 5th 2020 and May 5th 2020, we set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.set_options(pytrends_config={\"geo\": \"DE\", \"timeframe\": \"2020-03-05 2020-05-05\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run the construction! Which can take a while!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.create_anchorbank() # takes a while to run since it queries Google Trends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have another available anchor bank:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existing GTABs:\n",
      "\tgoogle_anchorbank_geo=IT_timeframe=2019-01-01 2020-08-01.tsv\n",
      "\tgoogle_anchorbank_geo=DE_timeframe=2020-03-05 2020-05-05.tsv\n",
      "\tgoogle_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n",
      "Active anchorbank: google_anchorbank_geo=_timeframe=2019-01-01 2020-08-01.tsv\n"
     ]
    }
   ],
   "source": [
    "t.list_gtabs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can set it as default and make new queries, now in this new location/timeframe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active anchorbank changed to: google_anchorbank_geo=DE_timeframe=2020-03-05 2020-05-05.tsv\n",
      "\n",
      "Using example_path/output/google_anchorbanks/google_anchorbank_geo=DE_timeframe=2020-03-05 2020-05-05.tsv\n",
      "New query '/m/0jg7r'\n",
      "New query calibrated!\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_ratio</th>\n",
       "      <th>max_ratio_hi</th>\n",
       "      <th>max_ratio_lo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.318772</td>\n",
       "      <td>0.301391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.470569</td>\n",
       "      <td>0.449615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-07</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.693204</td>\n",
       "      <td>0.667012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.005060</td>\n",
       "      <td>-0.004941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.288413</td>\n",
       "      <td>0.271746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.308653</td>\n",
       "      <td>0.291509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-02</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.308653</td>\n",
       "      <td>0.291509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.622365</td>\n",
       "      <td>0.597840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.268174</td>\n",
       "      <td>0.251982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.268174</td>\n",
       "      <td>0.251982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_ratio  max_ratio_hi  max_ratio_lo\n",
       "date                                             \n",
       "2020-03-05       0.31      0.318772      0.301391\n",
       "2020-03-06       0.46      0.470569      0.449615\n",
       "2020-03-07       0.68      0.693204      0.667012\n",
       "2020-03-08       0.00      0.005060     -0.004941\n",
       "2020-03-09       0.28      0.288413      0.271746\n",
       "...               ...           ...           ...\n",
       "2020-05-01       0.30      0.308653      0.291509\n",
       "2020-05-02       0.30      0.308653      0.291509\n",
       "2020-05-03       0.61      0.622365      0.597840\n",
       "2020-05-04       0.26      0.268174      0.251982\n",
       "2020-05-05       0.26      0.268174      0.251982\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.set_active_gtab(\"google_anchorbank_geo=DE_timeframe=2020-03-05 2020-05-05.tsv\")\n",
    "mid = \"/m/0jg7r\" # freebase code for EPFL\n",
    "nq_res = t.new_query(mid) \n",
    "nq_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gtab",
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   "name": "gtab"
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  "language_info": {
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