Created
November 17, 2021 14:44
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A jupyter notebook with Google Trend analysis on a topic plotted with plotly.
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Test it\n", | |
| "[datalore](https://datalore.jetbrains.com/notebook/XKSXNL66DlEB7PwmhUEWRy/0910N4XZh2E9gLjQUNrVAV/)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Install the Modules\n", | |
| "!pip install pytrends\n", | |
| "!pip install plotly\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Create Object\n", | |
| "Create Object and import libraries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from pytrends.request import TrendReq # For Trend analysis\n", | |
| "import plotly.express as px # For ploting it\n", | |
| "\n", | |
| "pytrends = TrendReq(hl='en-US', tz=360) # Initialize the trend object" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# build payload\n", | |
| "\n", | |
| "kw_list = [\"machine learning\"] # list of keywords to get data \n", | |
| "\n", | |
| "pytrends.build_payload(kw_list, cat=0, timeframe='today 12-m') # create payload" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "config": { | |
| "plotlyServerURL": "https://plot.ly" | |
| }, | |
| "data": [ | |
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| "hovertemplate": "variable=machine learning<br>date=%{x}<br>value=%{y}<extra></extra>", | |
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| "#1 Interest over Time\n", | |
| "data = pytrends.interest_over_time() \n", | |
| "data = data.reset_index()\n", | |
| "\n", | |
| "fig = px.line(data, x=\"date\", y=['machine learning'], \n", | |
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