{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Downloading Google Trends Data (Python version)\n", "Google trends data can be quite usefull to understand behavioral differences in response to a public health risk, particulary when it comes to gauging the perception of such risk.\n", "\n", "The `ggtrends` module from Epigraphhub returns the results as convenient pandas DataFrames.`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-04-21T07:53:54.006924Z", "start_time": "2022-04-21T07:53:53.830787Z" }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from epigraphhub.data.ggtrends import (\n", " historical_interest,\n", " interest_by_region,\n", " interest_over_time,\n", ")\n", "import pandas as pd\n", "pd.options.plotting.backend = \"plotly\"\n", "import warnings\n", "warnings.filterwarnings('ignore')\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fetch trends by region" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-04-21T07:53:58.206929Z", "start_time": "2022-04-21T07:53:57.102865Z" }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
| \n", " | geoCode | \n", "covid | \n", "vaccine | \n", "
|---|---|---|---|
| geoName | \n", "\n", " | \n", " | \n", " |
| Afghanistan | \n", "AF | \n", "65 | \n", "35 | \n", "
| Albania | \n", "AL | \n", "85 | \n", "15 | \n", "
| Algeria | \n", "DZ | \n", "97 | \n", "3 | \n", "
| American Samoa | \n", "AS | \n", "79 | \n", "21 | \n", "
| Andorra | \n", "AD | \n", "98 | \n", "2 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "
| Western Sahara | \n", "EH | \n", "93 | \n", "7 | \n", "
| Yemen | \n", "YE | \n", "62 | \n", "38 | \n", "
| Zambia | \n", "ZM | \n", "74 | \n", "26 | \n", "
| Zimbabwe | \n", "ZW | \n", "84 | \n", "16 | \n", "
| Åland Islands | \n", "AX | \n", "86 | \n", "14 | \n", "
250 rows × 3 columns
\n", "