Google Trends is a mechanism that serves to discover user interest and search traffic relating to a topic or a specific keyword. The disadvantage of this tool is that it allows you to perform one search at a time and, although this includes graphs and maps of pleasantness/interest of the topics and a series of keywords and related issues, it is a slow method. From the perspective of the SEO consultant who uses Google Trends to understand the nature of a market or apply knowledge to content creation, there is a need to aggregate data differently. From here on, we’ll answer the question “how to use Pytrends.”
For this reason, we tend to use tools that use programming languages, such as python, which take care of aggregating data in the best possible way. For this reason, the Google Trends API for Python, or PyTrends, is born. Below are ways you can use Pytrends for applying knowledge.
What is Pytrends?
It is an interface to download Google Trends data and generate reports in CSV format (thanks to the Pandas library’s presence). You are not an official API (but providing a beneficial service in any case). Unfortunately, the documentation is somewhat scattered.
The main functions that concern installation and implementation on a platform can be found in the Pytrends GitHub page (s) (there are many but all quite valid). The first impact with the code is well regulated, so the problem arises when deciding how to extract the data. Knowledge of Python and the libraries used is taken for granted.
Therefore, the real stumbling block concerns the creation of usable space and a series of repeatable and easy-to-download routines. Precisely for this reason, we chose to provide guidance and code development on Google Colab.
How to Install Pytrends
You can use Pytrends on Linux, Mac OS, or Windows by installing python.
What you need is:
- Python 2.7+ and 3.3+
make sure you don’t use outdated versions of Python.
- Requests, lxml, Pandas
Other libraries such as Requests, etc.
- Optional Jupyter notebook
Or anything of the sort.
After opening the Jupyter notebook, all you have to do is type.
pip install pytrends From pytrends.request import TrendReq Import pandas as pd Import time Import datetime From datetime import datetime, date, time Pytrend = TrendReq() Pytrend.build_payload(kw_list=['vlog','blog'], timeframe='today 4-y' , geo ='ID')
For this code, you can change the password, time, and geography according to your wishes.
To see the data, type
Interest_over_time df = pytrend.interest_over_time() print(interest_over_time_df.head())
After that, the data will appear. To display the data in CSV format, you can type the code.
The data will be saved with the blog name keyword.csv. Now you can also display the data with a graph or without having to look at the CSV. The trick is to type the code.
Import matplotlib.pyplot as plt Import seaborn as sns sns.set(color_codes=true) Dx = Interest_over_time_df.plot.line(figsize= (8,6), title=("Interest Over Time") dx.set_xlabel('Date') dx.set_ylabel('Trends Index')
After that, the graph will appear.
You can also view related queries from Google Trends. The trick is to type the code below.
Pytrend.build_payload(kw_list=['hosting','vps','cloud], timeframe='today 11-m') Rq = pytrend.related_queries()
Later, the keywords that are related to the keywords you enter will appear. These keywords can be changed according to your wishes.
You can also see which keywords are on the rise. The trick is to type the code,
Pytrend.build_payload(kw_list=['hobby','hobby','delivery,' recipes'], geo='id' , timeframe='today 3-m') Related_queries = pytrend.related_queries() Dg = related_queries.get('recipes').get('rising') Dg
After that, it will display any queries that have been going up for the last three months. You can use this query for your articles.
You can also save the data to CSV with code
FAQ About How to Use Pytrends
No, the Google Trends tool allows you to perform one search at a time. This is one of the tool’s disadvantages.
The Google Trend tool, when used, is usually slow due to the graphs and maps of pleasantness/interest of the topics and a series of keywords and related issues it includes.
You can find the main functions that concern installation and implementation on a platform on the Pytrends GitHub page.
In any case, the first impact with the code is well regulated. The problem arises when deciding how to extract the data.
Yes, you can install and use Pytrends on Linux, Mac OS, or Windows by installing python.
How to Use Pytrends in Short
Even though Google trends uses Pytrends to generate reports in CSV format, there are steps you can take for completing coding tasks. Anyone can use Pytrends via different platforms by installing python. Pytrends is a very helpful tool for individuals who want to use Google Trends for discovering user interests and similar tasks. That’s why we believe it is important that you learn how to use Google Trends.