gtus 0.1.2

A Python package to collect and analyze Google Trends data for US states.

GTUS - Google Trends for US States

The GTUS package is a Python library designed to collect Google Trends data for specified queries across various U.S. states. It provides functionality to handle multiple queries and states efficiently, while adhering to Google Trends API limitations.

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Features

• Collect trends for one or multiple states.
• Batch processing of queries to avoid API limits.
• Adjustable wait time between requests to manage rate limits.
• Export collected data to Excel and JSON formats.
• Access data in a consolidated pandas DataFrame for easy analysis.

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Installation

To use the GTUS package, ensure you have the required dependencies installed. You can install them using pip:

pip install gtus

This will automatically install all required dependencies, including pytrends, pandas, and aiohttp.

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Getting Started

Here's how to start using GTUS to collect Google Trends data.

Importing the Package

To use the GTUS package, import it as follows:

from gtus import GTUS

1. Collect Google Trends Data for US States

Collecting for One State

To collect data for a single state, create an instance of the GTUS class with the desired query and state:

from gtus import GTUS

queries = ["telemedicine", "remote work"]
states = ["TX"]
gtus = GTUS(queries, states, timeframe="2022-01-01 2023-01-01")

# Collect data
gtus.collect_all_trends()

Collecting for Multiple States

To collect data for multiple states, simply provide a list of states:

from gtus import GTUS

queries = ["telemedicine", "remote", "football", "dance"]
states = ["CA", "NY", "TX"]
gtus = GTUS(queries, states, timeframe="2020-01-01 2023-01-01")

# Collect data
gtus.collect_all_trends()

Collecting for All States

If no states are specified, GTUS will automatically collect data for all US states:

queries = ["remote work", "telehealth"]

# Initialize GTUS object
gtus = GTUS(queries=queries, timeframe="2022-01-01 2023-01-01", wait_time=15)

Modifying Wait Time

You can adjust the wait time between requests by specifying the wait_time parameter when creating the GTUS instance. This is useful for managing API rate limits:

gtus = GTUS(queries, states, wait_time=15)  # Set wait time to 15 seconds

Saving Data

Exporting to Excel

To save the collected data to an Excel file, use the export_to_excel method:

gtus.export_to_excel("google_trends_data.xlsx")

Exporting to JSON

To save the collected data to a JSON file, use the export_to_json method:

gtus.export_to_json("google_trends_data.json")

Accessing Data in Python

The data collected can be accessed in a consolidated pandas DataFrame. This allows for easy manipulation and analysis:

df = gtus.create_consolidated_dataframe()
print(df.head())

Example Output

The DataFrame will have the following structure:

date	State	telemedicine	remote work	football	dance
2021-12-26	CA	72.0	62.0	59.0	68.0
2021-12-26	TX	73.0	59.0	67.0	70.0

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Dependencies

• pandas
• pytrends
• aiohttp
• openpyxl

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Contributing

Contributions are welcome! Please submit issues or pull requests via GitHub.

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License

This project is licensed under the MIT License. See the LICENSE file for more information.

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Start exploring Google Trends data with GTUS today!