Pytrend SEO Usage

PyTrend Guideline: Create Dashboard for Google Trends with Python

Google Trend is an application of Google which shows the search terms’ trends over time for different geographies. It shows real-time search trends and daily search trends for every city in the world. Thanks to Google Trend, understanding the users’ intent through different seasons for different queries is easier. Also, real-time events and real-world entities can be noticed via Google Trend earlier. In PubMed (Medical Publication Site), there are lots of studies show that Google Trends can indicate outbreaks’ early stages to let governments take measures. Lastly, Google Trends help AdWords and SEO campaigns to see Brand Power and related queries’ trends over time for creating a smart campaign strategy.

According to Google, thanks to Google Trends, all popular queries can be estimated with pre-search volumes with a maximum margin of 12%. While consistency was lower in entertainment and social network queries, this consistency was almost over 90% in the health, travel, and service sectors. Because of this situation, following Google Trends Data with Python in bulk can be helpful for an SEO. Also, in this article, we will learn how Google classifies search terms according to different industries and search intents so that we can clean our data even more.

To learn more about Python SEO, you may read the related guidelines:

  1. How to resize images in bulk with Python
  2. How to perform TF-IDF Analysis with Python
  3. How to crawl and analyze a Website via Python
  4. How to perform text analysis via Python
  5. How to test a robots.txt file via Python
  6. How to Compare and Analyse Robots.txt File via Python
  7. How to Categorize URL Parameters and Queries via Python?
  8. How to Perform a Content Structure Analysis via Python and Sitemaps
  9. How to Check Grammar and Language Errors with Python
  10. How to scrape People Also Ask for Questions from Google via Python
  11. How to check Status Codes of URLs in a Sitemap via Python
  12. How to Categorize Queries with Apriori Algorithm and Python

What is PyTrend?

PyTrend is a Python library for using Google Trends API with Python. It allows us to produce more data faster. Also, it creates a chance to draw interactive plots for searched terms’ trend graphics over the selected time periods. Changing language, industry, geography in Pytrend is also possible. PyTrend is not an official API of Google. Also, using different time zones, extract low search volume countries, finding related queries, and rising topics are doable thanks to PyTrend.

We can start our guideline for PyTrend.

How to Install PyTrend?

The necessary code line for installing PyTrend is “pip install pytrends”.

To use PyTrends, you need to have Pandas, Requests, lxml dependencies in your Python Environment.

How to Pull Data from Google Trends via PyTrend?

To pull data from Google Trends via Python, user has to connect to the Google.

from pytrends.request import TrendReq
pytrends = TrendReq(hl='es-US', tz=360)

With these two lines of code, connecting the Google Trends is possible. “hl” stands for “Host Language” while “tz” stands for “time zone”. Google uses Central Standard Time, CST for time management. So while using PyTrends, we need to use CST instead of Coordinated Universal Time, UTC.

Using PyTrends via proxies is also possible. Since some users need to make more requests, sometimes Google Trends may block the user, the proxy is a workaround for this reason.

from pytrends.request import TrendReq
pytrends = TrendReq(hl='en-US', tz=360, timeout=(3,12), proxies=['https://192.128.156.13:80',], retries=1, backoff_factor=0.1, requests_args={'verify':False})
  • The timeout attribute is from the Requests Library of Python. The first argument is for “connecting”, the second one is for “read”. If a connection try lasts more than 3 seconds and a reading of the request lasts more than 12 seconds, it will give a timeout error.
  • Proxies attribute is for stating the proxies which will be used for the request, the port number is obligatory.
  • “Retries” is the number of retrying chances after the first failed request.
  • Requests_args parameter is to comply with the nature of the request, false means ignore SSL.
  • Backoff_factor is the time period between Retries.

After the connection has been established, we can continue to the next section: pulling data.

How to Pull Keyword Trends with PyTrend from Google Trends

To pull data from the Google Trends, we need to determine which keywords we want to pull data about. To state that, we will create a variable below:

kw_list = ['coronavirus', 'donald trump', 'joe biden', 'china', 'elections']
pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')
  • “timeframe=’today 5-y'” means 5 years from today,
  • “geo” attribute is for determining the geography for the data which will be pulled.
  • “group” stands for Google Property which means that which vertical search you want to use such as google images, web searches or news, youtube etc…

Thanks to these two lines of code, we can be set for pulling data for the queries in “kw_list” variable.

How to Check Interest Over Time for Given Queries in PyTrend

To check the interest over time, we need to use the method with the same name which is “interest_over_time()”. You may see the result below.

pytrends.interest_over_time()
OUTPUT>>>

coronavirusdonald trumpjoe bidenchinaelections
dateisPartial
2015-07-2600020False
2015-08-0201020False
2015-08-0901020False
2015-08-1601020False
2015-08-2301020False
2020-06-14131030False
2020-06-21131020False
2020-06-28131030False
2020-07-05131020False
2020-07-12131020False
260 rows × 6 columns

From 5 years today, we have taken the Trends Information for the given queries. As we may see that in 2015, Coronavirus has a 0 interest over time but in 2020 it starts to increase its trend curve. Also, Donald Trump is always between 0-1 and Joe Biden is always 0. So, as you may see there might be some misunderstanding here. Let’s check together.

First of all, we have used the Search Term instead of the Topic or Entity Profile, that’s why the information in the table for Political Figures are not reflected as correct. Second, we have used “0” for “cat” attribute. Google Trends classifies queries according to the their search intent and checking these trend classes can be useful to understand Google’s perception for categorization.

Content Categories according to Google
For the rest of categories: https://github.com/pat310/google-trends-api/wiki/Google-Trends-Categories

According to the Categorization, “Politics” has the number 396. If we change the our categorization and timeline like below:

pytrends.build_payload(kw_list, cat=396, timeframe='today 3-m', geo='US', gprop='')

coronavirusDonald TrumpJoe BidenChinaElections
dateisPartial
2020-04-2137314517False
2020-04-2227411315False
2020-04-2332515715False
2020-04-2428714515False
2020-04-2521313410False
2020-07-1418619238False
2020-07-1519721249False
2020-07-1613519323False
2020-07-1710516125False
2020-07-1815818319False
89 rows × 6 columns

In this example, we have only pulled the data for “Politic Web Searches” for the last 3 months from the United States. But still, our data is not like completely realistic. It is closer to reality than before but, still now enough. To see the reason for this, we need to use different variations for our keyword list.

First, let’s change our category back to the 0 which means all categories:

pytrends.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='GB')
pytrends.interest_over_time()

CoronavirusTrumpJoe BidenChinaElections
dateisPartial
2020-04-211003030False
2020-04-22683040False
2020-04-23673040False
2020-04-249314030False
2020-04-25679040False
2020-07-14283030False
2020-07-15244030False
2020-07-16244030False
2020-07-17253030False
2020-07-18224030False
89 rows × 6 columns

Now, you may see that Coronavirus search trends is way much more realistic. It has a peak during April and in the summer months it has decreased its trend. But, still our political figures don’t have so much attention like always do. Let’s change our keyword list this time:

kw_list = ['Donald Trump']
pytrends.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='GB')

Donald Trump
dateisPartial
2020-04-2121False
2020-04-2223False
2020-04-2320False
2020-04-24100False
2020-04-2562False
2020-07-1419False
2020-07-1524False
2020-07-1618False
2020-07-1718False
2020-07-1818False
89 rows × 2 columns

Data is way much more realistic. Now, we can check Donald Trump’s and Joe Biden’s trend curves at the same time.

kw_list = ['Donald Trump', 'Joe Biden']
pytrends.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='GB')
Donald TrumpJoe BidenisPartial
date
2020-04-21212False
2020-04-22241False
2020-04-23212False
2020-04-241003False
2020-04-25584False
2020-07-14182False
2020-07-15242False
2020-07-16172False
2020-07-17164False
2020-07-18182False
89 rows × 3 columns

It is not realistic again, Donald Trump’s trend curve has been changed and also Joe Biden’s trend curve is so much weaker than the real. What is the reason for this? First, the “isPartial” section may catch your attention, it is not related to this situation but it shows whether the data includes estimations or not. The real reason for this difference is that every search term and the entity has its own curve as it is but if you unify the queries in a list, they will change their curve in a competition with a relativist way.

Let’s check Joe Biden’s trend curve just only for him:

kw_list = ['Joe Biden']
pytrends.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='GB')
pytrends.interest_over_time()

Joe Biden
dateisPartial
2020-04-2116False
2020-04-2210False
2020-04-2315False
2020-04-2430False
2020-04-2528False
2020-07-1419False
2020-07-1525False
2020-07-1614False
2020-07-1725False
2020-07-1822False
89 rows × 2 columns

It is way much different, because it has been calculated with only its own values.

What is the solution for this problem? How can we calculate the trend curve one by one for every query? With a for loop. You may follow the code below to perform same process for each query one by one.

Let’s begin:

First, we need to create our “keyword list” which consist of our targeted search terms.

kw_list = ['Joe Biden', 'Donald Trump', 'Hillary Clinton', 'Bernie Sanders', 'Elizabeth Warren', 'Jane Sanders', 'Tulsi Gabbard', 'Barack Obama']

Now, we need to create a nested list so that “build_load()” method can deal with every query one by one.

kw_group = list(zip(*[iter(kw_list)]*1))
print(kw_group)
OUTPUT>>>
[('Joe Biden',), ('Donald Trump',), ('Hillary Clinton',), ('Bernie Sanders',), ('Elizabeth Warren',), ('Jane Sanders',), ('Tulsi Gabbard',), ('Barack Obama',)]

With this code, we have turned our list into a tuple collection in a list. Iter is being used for “iteration”, “zip” is being used for creating tuples which from the elements of different variables. In this example, since we have only one list, it has created a tuple collection only from one variable.

Now, we need to turn every tuple into a list, we will use the List Comprehension Method.

kw_grplist = [list(x) for x in kw_group]
print(kw_grplist)
OUTPUT>>>
[['Joe Biden'], ['Donald Trump'], ['Hillary Clinton'], ['Bernie Sanders'], ['Elizabeth Warren'], ['Jane Sanders'], ['Tulsi Gabbard'], ['Barack Obama']]

Since, every search term in a list of lists, build_payload() method will have to check every query one by one through our for loop.

from pytrends.request import TrendReq
import pytrends
import pandas as pd
trendshow = TrendReq(hl='en-US', tz=360)
dict = {}
i = 0
for kw in kw_grplist:
    trendshow.build_payload(kw, timeframe = 'today 3-m', geo='US')
    dict[i] = trendshow.interest_over_time()
    i += 1

trendframe = pd.concat(dict, axis=1)
trendframe.columns = trendframe.columns.droplevel(0)
trendframe = trendframe.drop('isPartial', axis = 1)
trendframe

The explanation of the code above is below.

  • First-line imports the necessary method for connecting with Google Trends.
  • Second-line imports the library itself.
  • The third line imports the Pandas Library for creating a data frame.
  • The fourth line connects with Google Trends with determined language and timezone.
  • The fifth and sixth lines create an empty dictionary to append our results and a variable that is equal to 0 for incrementing it through the loop.
  • The seventh, eighth, ninth, tenth lines consist of for loop’s itself. We simply use every search term from our list one by one to get their information without any bias. After getting the information, we are appending it to our empty dictionary.
  • In the 11th, 12th, 13th, and 14th lines, we are combining our dictionary elements in a data frame in through columns (axis=1) and dropping the unnecessary multi-index lines along with the “isPartial” column.

You may see the result below:


Joe BidenDonald TrumpHillary ClintonBernie SandersElizabeth WarrenJane SandersTulsi Gabbard
dateBarack Obama
2020-04-2124307517364314
2020-04-2228256527181911
2020-04-232423649100535413
2020-04-24253674737353113
2020-04-2544347601404811
2020-07-1537341731506115
2020-07-1635311429704512
2020-07-1732291133903411
2020-07-18383612368242221
2020-07-19363412277243913
90 rows and 8 columns exist in the data table as output of our PyTrend request.

Now, as you can see, every search term has its own trend curve as much as realistic. We can also change the search terms’ category so that we can see which of those political figures are subject of the Political Humor.

We need to add a small part to the our code to check this.

for kw in kw_grplist:
    trendshow.build_payload(kw, timeframe = 'today 3-m', geo='US', cat=1180)
    dict[i] = trendshow.interest_over_time()
    i += 1

We have added the “cat=1180” section which tells that we only want to check the “Political Humor” trends for those names.


Joe BidenDonald TrumpHillary Clinton
2020-04-2119033Barack Obama
2020-04-22150042
2020-04-2319000
2020-04-24330080
2020-04-2522000
2020-07-153231370
2020-07-16380370
2020-07-17170390
2020-07-18760054
2020-07-19130054
90 rows and 4 columns exist in the data table as output of our PyTrend request.

As you may see, our column number has been decreased because there was no search or trend curve for the rest. Also, we see that Joe Biden’s trend for Political Humor is dominant for his search profile on Google. You can also change the “gprop” attribute to see which one of these names has the best coverage in the “News” or “Images” on Google.

Also, in the future, we will try to create sentiment analysis analysing through tweets and search results related to the certain entities, this process then, will be even more fun.

How to Visualize the Trend Curves for Different Search Terms via Python and PyTrend

We can also create different types of graphical expressions of this data extractions. To create an interactive graphic, we will use plotly, plotly graph objects and plotly offline.

import plotly
import plotly.graph_objects as go
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
import plotly.offline as pyo
init_notebook_mode(connected=True)

trace = [go.Scatter(
x = trendframe.index,
y = trendframe[col], name=col) for col in trendframe.columns]

data = trace
layout = go.Layout(title='Post', showlegend=True)
fig = go.Figure(data=data, layout=layout)

iplot(fig)

You may see the result of this graphic below:

Trend Comparison between different Political Leaders in the US.

If you find this look complicated, you may create barplots with lesser amount of columns or you can try to use subplots for seperating every data visualization from each other. At below, you will see an example for search trend comparison between Joe Biden and Donald Trump in bar plots.

trace = [go.Bar(
x = trendframe.index,
y = trendframe[col], name=col) for col in trendframe.columns[0:2]]

data = trace
layout = go.Layout(title='Post', showlegend=True)
fig = go.Figure(data=data, layout=layout)

iplot(fig)

With the “trendframe.columns[0:2]”, we have pulled the first two columns for the comparison.

You may see the trend difference between Joe Biden and Donald Trump via PyTrend and Plotly.

We will have more guidelines for creating different and more efficient visualization techniques for creating more insightful SEO and Data Graphics with Python. Now, we can continue to the Pytrend.

How to Pull Search Trends Hourly with Pytrend

PyTrend has another method which is get_historical_interest(). Thanks to this method, we may determine the start and end year, month, day and hour for getting the trend data. I have used the same for loop for getting hourly trends.

from pytrends.request import TrendReq
import pytrends
import pandas as pd
trendshow = TrendReq(hl='en-US', tz=360)
dict = {}
i = 0
for kw in kw_grplist:
    trendshow.get_historical_interest(kw, year_start=2020, month_start=7, day_start=15, hour_start=0, year_end=2020, month_end=7, day_end=17, hour_end=23, cat=0, geo='US', gprop='', sleep=0)
    dict[i] = trendshow.interest_over_time()
    i += 1

trendframe = pd.concat(dict, axis=1)
trendframe.columns = trendframe.columns.droplevel(0)
trendframe = trendframe.drop('isPartial', axis = 1)
trendframe

“trendshow.get_historical_interest(kw, year_start=2020, month_start=7, day_start=15, hour_start=0, year_end=2020, month_end=7, day_end=17, hour_end=23, cat=0, geo=’US’, gprop=”, sleep=0)” section actually tells itself. I have started pulling data from the 7th Month’s 15th day of first hour until the 7th Month 17th day of last hour. “Sleep” attribute in the get_historical_interest() method is just about determining the waiting time after failed requests from Google.

PyTrend Dashboard
You may see the Trend Data based on the dates we have determined.

You may see the result above. We have specific 3 days data from the a certain time period. You can also check the graphic below:

You may see the trend differences between Joe Biden, Donald Trump, Hillary Clinton and Bernie Sanders in the given time.

How to Check Search Terms’ Interest According to Region by PyTrend

PyTrend also has a special method which is related to the geographic queries.

pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False)
  • Resolution can be “city”, “country”, “region” according to the targeted area.
  • “inc_low_vol” is for including the low volume search data from specific regions.
  • “inc_geo_code” is for including the ISO Code of Countries in the data along with their names.

You may see the results for the last three months of search trends for Donald Trump and Joe Biden.

kw_list = [‘Joe Biden’, ‘Donald Trump’]
trendshow.build_payload(kw_list, cat=0, timeframe=’today 3-m’, geo=’US’)
trendshow.interest_by_region(resolution=’COUNTRY’, inc_low_vol=True, inc_geo_code=False)

PyTrend Results for only one country
We only see the United States’ sections for trend comparison, thanks to “resolution=’Country'” attribute.

You may also put this into a Map Graphic thanks to Geo-Pandas Library, or you may create a for loop for every different city with subplots.

How to Find Related Searches, Topics and Search Suggestions with PyTrend

Thanks to PyTrend, a Holistic SEO can create his/her own dashboard for a more comprehensive industry and user behavior analysis. The latest trends, search suggestions and related topics for a specific entity can be useful for creating more authority, revenue, traffic and relevance. Content marketing, Brand Language and Communication, AdWords Campaigns and even the On-Page SEO Elements can be optimized according to the latest and the most relevant search activities.

How to find related topics with Pytrend?

pytrends.related_topics() method is for finding the most relevant topics for a specific entity. I recommend you to use this for only one query, if you use more than one query, PyTrend will try to find an intersection between queries. And mostly, this process makes no result.

kw_list = [‘Donald Trump’]
trendshow.build_payload(kw_list, cat=0, timeframe=’today 3-m’, geo=’US’)
print(trendshow.related_topics())

The most related topics, entities and their types with Donald Trump is below:

PyTrend Related Topics
We see lots of different and related entities and their entity types.

As we can see, related topics consist of entities, not search terms. So, a Holistic SEO can easily see the relationship between entities and their association type via PyTrend. Also, scraping Entity IDs thanks to PyTrend is possible:

Pytrend Usage with Related Topics
We have related entities and their entity id’s this time.

These Entity IDs can help an SEO to use Google’s Knowledge Graph API with Python easier.

How to find related queries with PyTrend?

If you wonder the difference between a query and a entity, you need to understand the difference between meaning and string. Now, we will see related strings, instead of entities.

The necessary method for this is “related_queries()”.

kw_list = ['Donald Trump']
trendshow.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='US')
trendshow.related_queries()

You may see the most related queries for Donald Trump. Also, you can easily see the difference between an Entity and a Query here. And you may understand why Google needs to understand both of them at the same time. Queries can change the meaning and content of the entities, they can be in a enormous variety and include the smallest details.

Related Queries Method of PyTrend
You may see the related queries with “Donald Trump” for the given time space.

Also, you can get the “rising keywords” for a specific query with this method.

Rising Keywords of PyTrend
Rising Keywords Method gives us the rising trends for the given Query/Entity.

The numbers here can show the increased interest over some queries. For instance, “Donald Trump George Floyd” query is searched more than %66300 in last 3 months for previous timelines. This may help for managing SEO and AdWords Campaigns in many way, from understanding the users and optimizing the content and communication.

How to find Trending Searches with PyTrend?

With PyTrend, we may pull the most searched and trending queries for a region. The required method is “trendging_searchers()”.

trendshow.trending_searches(pn='united_kingdom')
PyTrend Trending Searches
You may see the trending searches for a given day and country as above.

These are the trending searches for United States from 21 July 2020.

How to find suggested topics and queries with PyTrend?

The related method for this is “suggestions(query)”.

dp_df = trendshow.suggestions('donald trump')
dp_df = pd.DataFrame(a)
dp_df
PyTrend Suggestions
We have suggested entities for the Donald Trump, these are the most important topics for getting the latest events and identity definition of the entity.

You may see the suggested profiles, searches and features for the query ‘donald trump’. Google gives the entity IDs, entity types for defining the searched query. This data may help a Holistic SEO in his/her content strategy, for a targeted topic and main query, you may need to understand what Google thinks and how Google defines the entity/query. What is the most relevant sides, features and past events for this query? What is the most related other entities? This same process can be done also for “soup”.

sp_df = trendshow.suggestions('soup')
sp_df = pd.DataFrame(sp_df)
sp_df
midtitletype
0/m/01z1m1xSoupType of dish
1/m/09gmsRamenDish
2/m/0ckmpChicken soupSoup
3/m/04zwg_Lentil soupFood
4/m/03qj953Cream of mushroom soupFood
Entity IDs and Entity Types from PyTrend

If you want to rank yourself in the “soup” query, you may need to include these terms, topics, and related information in a structured way in your content within different formats.

How to pull the most searched trendy queries for a specific month for a country with PyTrend?

The necessary method for this task is the “top_charts()”.

pytrends.top_charts(date, hl='en-US', tz=300, geo='GLOBAL')

The date has to be in YYYY or YYYY-MM format like 2017 or 2017-5.

trendshow.top_charts(2020-7, hl='en-US', tz=360, geo='TR')
PyTrend Top Charts
We have called the top searched queries in Turkey with American English in the Seventh month of 2020 based on global timezone.

These are the most searched and trendy queries in the US for the 7th months of 2020. I must say that this article is being written on 21st July of 2020, it means that this includes only the first 21 days. Also, if you wonder how to pull all the categories in PyTrend, you should use categories() method.

What is the Importance of Search Trends for SEO?

Search Trend Data is one of the most important signals to understand how the mass thinks. It also shows a methodology for analyzing the sentiment related to certain entities. A Holistic SEO can create his/her own Google Trends API thanks to Python and check the search trends along with the organic traffic. If there is a political crisis or pandemic, how people search and behave on the web? If there is a vacation, what people search for? All of these data can be accessed via PyTrend easily and create insight for Holistic SEO.

Also, what is the most related query for an entity? What features do an entity have for specific search intent? Creating a content strategy, understanding the target market, and users’ behavior models during different seasonal and un-ordinary events/situations, managing brand communication, and community management are important pillars for Holistic SEO.

For now, our PyTrend Guideline has tons of missing points. In time, we will refresh and improve it.

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