A Comprehensive Guide to Using the Pytrends Python Library

Introduction

With Pytrends, users can easily visualize the data they have collected, as well as create custom reports and dashboards. Additionally, Pytrends offers tutorials and documentation to help users get started with the library. In this article, we will discuss what Pytrends is and how it works so that you can start using it for your own data analysis projects.

How to Install & Set Up the Pytrends Python Library

Installing and setting up the Pytrends Python library is easy and straightforward.

Here, is the pip command that you need to run for installation

pip install pytrends

Exploring the Different Features of the Pytrends Python Library

Connecting to google

One of the initial steps for using pytrends is to connect it with Google and we need to import TrendReq from pytrends.request. Also, pandas library for data visualization.

import pandas as pdfrom pytrends.request import TrendReqpytrend = TrendReq()

Intreset By Region

Let's say we need to find the intreset of people of different countries in a given keyword. For this, we will use interest_by_region method to gennerate a dataFrame( which shows what proportions of people are interested in the given topic).

kw = ['Python']pytrend.build_payload(kw_list=kw)df = pytrend.interest_by_region()df.head()

The values goes from 0 to 100.

Dialy Trends

Now let us get the top daily search trends worldwide. To do this we have to use the trending_searches() method. If you want to search worldwide just don't pass any parameter.

Pytrends API gives the facility to checkout the dialy search trends from worldwide. trending_searches method we get a report of trending searches but for a country just pass parameter pn

df = pytrend.trending_searches(pn="india")df.head()

You should enter the country name in lowercase

Past trends

With top_charts method we can get the top trending searches yearly.

df = pytrend.top_charts(2022, hl="en-IN", tz=300, geo='GLOBAL')df.head()

Keyword Suggestion

Whenever we search on google, it will start giving suggestions according to the query given. To generate a list of suggestions, we need suggestions method.

keyword = 'Python'data = pytrend.suggestions(keyword)df = pd.DataFrame(data)df.head()

Related Queries & Topics

Related queries means the queries related to the keyword.

pytrend.build_payload(kw_list=['Python'])related_queries = pytrend.related_queries()related_queries.values()related_topic = pytrend.related_topics()related_topic.values()

Thanks for reading.