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codewithpom/Google-Trend-Single-Graph.ipynb

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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Google-Trend-Single-Graph.ipynb
{
"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"
]
},
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"source": [
"#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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}
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