README

Use this project to search Google Trends website for downloading the frequency or usage of keywords (search terms) that are of your interest (max 5 at a time).

Even though pytrends package can do similar tasks, my problem is something different and therefore I quickly built this project to solve it. Please modify the code as per your need.

Note:

• The resolution of trends searched will be at city level
• The Include low search volume regions checkbox will be selected by default
• If you don't want the driver browser in action, then in the file src/scrape_gtrends.py uncomment the line: # options.add_argument('--headless')

In the main.py file, under the section User Inputs, modify the values as per your need and save it.

# At least 1 and max of 5 keywords
keywords = ["bitcoin", "ethereum", "tether", "solana", "dogecoin"]

start_date = "2024-04-09"
end_date = "2024-05-09"

# For list of country names and their geocodes, check the file "utils/country_geo_codes.csv"
geo = "GB"

# Change this to actual path on your system to folder "Data_Output"
# Adjust the path format style based on the OS you are using (Linux, Windows, Mac)
csv_directory_location = '/home/xxxxx/xxxxx/GoogleTrends/Data_Output'

Open your terminal, run the following lines:

$ cd GoogleTrends

# It is highly recommended to create a virtual environment and install the following libraries
$ pip3 install -r requirements.txt

# Run the script
$ python3 main,py

In the above example:

1. the following csv files will be downloaded from Google Trends website to the folder Data_Output

	File	Notes
1	Data_Output/kwds_score_relative_each_other.csv	Compared breakdown by city
2	Data_Output/scores_for_kwd_bitcoin.csv	Interest by city for the word bitcoin
3	Data_Output/scores_for_kwd_ethereum.csv	Interest by city for the word ethereum
4	Data_Output/scores_for_kwd_tether.csv	Interest by city for the word tether
5	Data_Output/scores_for_kwd_solana.csv	Interest by city for the word solana
6	Data_Output/scores_for_kwd_dogecoin.csv	Interest by city for the word dogecoin

2. the files (above) will be parsed/cleaned and saved as two addition files

	File	Notes
7	Data_Output/cleaned_kwds_score_relative_each_other.csv	Compared breakdown by city (cleaned)
8	Data_Output/scores_for_kwds_combined.csv	Interest by city for each key word (cleaned and merged)