Google-Trend-Rank

A Python script that scrapes and analyzes data using Google Trends and various Python libraries.

📥 Installation Guide

Step 1 : Clone the Repository

Clone the repository using the following command.

$ git clone https://github.com/sbeen1840/Google-Trends-Analyzer.git

Step 2 : Install Dependencies

Note : It is recommended to use a conda environment with Python 3.6 with this code. Before running the commands in this guide, make sure you activate the environment using $ source activate <name of the env>

The use the requirements.txt file given in the repository to install the dependencies via pip.

$ pip install -r requirements.txt

Step 3 : Verify the installation of dependencies

To verify whether pandas, pytrends and trend were installed properly, run the following.

$ python
>>> import pandas
>>> import pytrends
>>> import trend

If there are no error messages upon importing the above dependencies, it would indicate that the they are correctly installed.

🔎 Usage

Step 1 : Update the keywords in the three CSV files.

1	2	3	4	5
JAVA	java
C++	c++
PYTHON	python	py	PY
javaScript	javascript	JAVASCRITP

Suppose you have a CSV file containing keywords up to five in one row.

The first index of each row becomes the representative keyword.

Keywords similar to the representative keywords may be entered in each row.

Step 2 : Change the route of csv file and set the themes of the files in main.py

csv1 = "C:/Users/user/Desktop/swa-java-2023/팀프로젝트/keyword_language.csv"
csv2 = "C:/Users/user/Desktop/swa-java-2023/팀프로젝트/keyword_jobgroup.csv"
csv3 = "C:/Users/user/Desktop/swa-java-2023/팀프로젝트/keyword_academy.csv"

theme1 = "프로그래밍언어"
theme2 = "개발직군"
theme3 = "개발교육"

Step 3 : Change the variables for data collection in trend.py file.

self.numb = 2 # 상위부터 추출 개수 
self.lang = 'ko' # pytrend 기준 언어 설정
self.time = 540 # pytrend 기준 시간대 설정
self.geo = 'KR' # pytrend 기준 위치 설정
self.month = 1 # 현재부터 n개월간의 기록 (정수만 입력)
self.update = 1 # 업데이트할 주기(단위 sec)  # 86400 하루
self.address = csv_address # main.py에서 지
self.dicts = 7 # json파일의 초기

Step 4 : Run the script by typing python main.py in the terminal.

Step 5 : Access the data by visiting http://localhost:5000/home in your web browser.

After running the script, you can access keywords representing the search trend by visiting http://localhost:5000/home in your web browser. You can also see their search volume, normalized. The data will be presented in the form of json and sorted in descending order.

💡 Results

Web json data http://localhost:5000/home
Console output

📑 Execution

docs/trend_exec.ipynb describes the execution steps of this program pipeline in detail.

📌 Notes

As of 2023/02/10, the above installation and execution steps are only tested on Window11. We will update as soon as we test the installation and execution steps on Linux and MacOS.

👤 Authors

• sbeen1840

🏷 License

• This project is licensed under the MIT License - see the LICENSE file for details

🙏 Acknowledgments

✍ References