Skip to content

Latest commit

 

History

History
117 lines (82 loc) · 2.51 KB

README.md

File metadata and controls

117 lines (82 loc) · 2.51 KB

PyTrendsPlus 📈📊

A Python library to fetch and analyze Google Trends data with additional functionalities such as data visualization 📊, keyword suggestions 💡, and trend predictions 🔮.

Installation 🛠️

To install the library, run the following command:

pip install pytrendsplus

Configuration

First, you need to import the library and create an instance of the PyTrendsPlus class:

from pytrendsplus import PyTrendsPlus

trends = PyTrendsPlus()

Usage

Fetch Google Trends Data

To fetch Google Trends data for a list of keywords and a specified time range:

keywords = ['Python', 'JavaScript']
time_range = '2020-01-01 2020-02-01'
data = trends.fetch_data(keywords, time_range)

Data Visualization

To visualize the fetched data as a line chart:

trends.plot_line_chart(data, title='Google Trends Interest Over Time')

Keyword Suggestions

To get keyword suggestions based on a given keyword:

keyword = 'Python'
suggestions = trends.get_suggestions(keyword)
print(suggestions)

Trend Predictions

To predict future trends based on the fetched data:

predictions = trends.predict_trends(data)
print(predictions)

Export data

To export the fetched data to a CSV or JSON file:

file_name = 'data.csv'
trends.export_data(data, file_name)

file_name = 'data.json'
trends.export_data(data, file_name)

Examples

To demonstrate the usage of the pytrendsplus library, you can create a script with the following code:

from pytrendsplus import PyTrendsPlus

# Create an instance of the PyTrendsPlus class
trends = PyTrendsPlus()

# Fetch Google Trends data
keywords = ['Python', 'JavaScript']
time_range = '2020-01-01 2020-02-01'
data = trends.fetch_data(keywords, time_range)

# Visualize the data as a line chart
trends.plot_line_chart(data, title='Google Trends Interest Over Time')

# Get keyword suggestions
keyword = 'Python'
suggestions = trends.get_suggestions(keyword)
print(suggestions)

# Predict future trends
predictions = trends.predict_trends(data)
print(predictions)

# Export the data to a CSV file
file_name = 'data.csv'
trends.export_data(data, file_name)

# Export the data to a JSON file
file_name = 'data.json'
trends.export_data(data, file_name)

Save this script as example.py and run it with the command:

python example.py

License

MIT License

Credits

Shout out to GeneralMills for creating the pytrends library which this library scaffolds off of

https://github.com/GeneralMills/pytrends