Use data to make SEO and marketing decisions

You will only need an internet connection and a Google account to access the interface. All the computing power is done directly on Google's servers.

By adapting a  script available on Google Trends , we are able to  "  replace  "  and complement the keyword generation tools.

To do this, we will use the Jupyter Notebook tool and the Python programming language and we will use the  pytrends  (the equivalent of a Google Trends API) and  panda  (for data manipulation via Python) libraries.

>> If you want to see the result,  click here .

Below, let's review the steps for getting started and using the working environment (installation, script and renders).

1. SETUP AND INSTALLATION

First, the installation of  pytrends  by the command below:

pip install pytrends 

Then installing  panda  with the same command specifying the  panda  library :

pip install panda 

Then, to load the libraries, copy/paste the code below and run it via Jupyter Notebook or Google Colab:

import pandas as pd 
from pytrends.request import TrendReq 
pytrend = TrendReq() 

We will start using this script via a first function allowing to have keyword suggestions based on Google Trends.  It is important to know that this data must be compared to other tools (Google Keyword Planner, Keywordtool.io, Ahrefs or SEMRush for example) if you want to obtain search volumes representing the market you are targeting.

2. KEYWORD SUGGESTION

keywords = pytrend.suggestions(keyword='mot clé')

The keywords  variable  is created by the  pytrend.suggestions function 

You just have to insert your keyword in the script and launch everything. You can  find examples  later in the article.

df_kw = pd.DataFrame(keywords)

We create the framework for the  df_kw  dataset from the created variable. You can name the dataset whatever you want.

df_kw.drop(columns= 'mid') 

Since there is often  “  noise  ”  in the result, we will remove one of the columns (the  'mid' column) using the drop module  .

After analyzing the possibilities of keyword suggestions, it may be interesting to seek a point of view on the trends of the day. This is what  pytrends  allows, and which we will cover in the next paragraph.

3. DAILY RESEARCH

df_popsearch = pytrend.trending_searches(pn='pays') 

The trending_searches module   of  pytrend allows to create the dataset framework called  df_popsearch  (  you can name the dataset whatever you want). Searches can be made by country via the  pn parameter . The country here is written in letters and not in ISO code .

df_popsearch.head() 

In order to validate if the result is suitable, we will only display the beginning via the  head() module

4. FIND THE "HOT" TOPICS OF THE MOMENT

Daily searches are good, but Google Trends also offers a  hot_topic module  that allows us to see what is currently the most searched for by Internet users (overall, in a theme, according to a country, etc.)

pytrend.build_payload(kw_list=['mot clé 1’, ‘mot clé 2'])

You can search by keyword or group of keywords for related hot topics . 

related_queries = pytrend.related_queries() 

We embed the  pytrend.related_queries() function  in a list named  related_queries. You can name this list whatever you want.

related_queries.values() 

Once configured, you must run the line above to get the return of the values. You will have a distinction between top searches ( top ) and rising searches ( rising ).

It is also possible for you to find out, for a chosen keyword, the  top  or  rising information  :

Related_queries['mot clé 1']['top'] 

5. TRENDING  TOPICS  LIVE FROM GOOGLE TRENDS

Google Trends is known for its  Trending Topics “ option  ” ; it is even the most used function of the website. In this case, we can explore it using  pytrends .  

df_todaytt = pytrend.today_searches(pn='CA') 

This piece of code allows us to know the  Trending Topics  of the day. You can manage the geographical part via the variable  pn=  (here  CA  for  Canada ). We save everything in a dataset named  df_todaytt.

6. SCRIPT EXAMPLES AND RETURNS

After all these code steps and possible uses of  pytrends , you are surely eager to know the rendering. You are in the right place.

• Keyword suggestions – Here is an example taken with the keyword  “  SEO  ”  :

It is of course necessary to pay attention to the noise of these requests ( Seoul  or  Securities and Exchange Organization  of Iran), because Google Trends remains less qualitative in its proposals (the objective for us being to have ideas of the market).

• Search Trends – Example from Canada:

According to Google Trends, search trends in Canada and at the beginning of April 2020 are as shown above. 

Note  : If you launch the script on your side, you may have different returns since search trends are linked to seasonality and the targeted country.

• Keywords related to SEO terms – Example taken with queries including the keyword  “  SEO  ”  :

dataframe.to_csv(‘./chemin/d-export/document.csv’, index = False, header = True)