How to Export Trending Data from Today's Searches¶
In [1]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
#get today's treniding topics
trendingtoday = pytrend.today_searches(pn='US')
trendingtoday.head(20)
Out[1]:
How to export Trend Data of Interest for Keywords by Region¶
In [2]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
#provide your search terms
kw_list=['Facebook', 'Apple', 'Amazon', 'Netflix', 'Google']
#get interest by region for your search terms
pytrend.build_payload(kw_list=kw_list)
df = pytrend.interest_by_region()
df.head(10)
Out[2]:
How to Export trending Searches in a Specific Country¶
In [4]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
# Get realtime Google Trends data
df = pytrend.trending_searches(pn='norway')
df.head()
Out[4]:
How to Get Keyword Suggestions¶
In [6]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
# Get Google Keyword Suggestions
keywords = pytrend.suggestions(keyword='innovation')
df = pd.DataFrame(keywords)
df.head(5)
Out[6]:
How to Get Related Queries / Topics + Download them as CSV¶
In [8]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
#provide your search terms
kw_list=['business intelligence', 'Data science', 'technology management', 'innovation', 'data governance']
pytrend.build_payload(kw_list=kw_list)
#get related queries
related_queries = pytrend.related_queries()
related_queries.values()
#build lists dataframes
top = list(related_queries.values())[0]['top']
rising = list(related_queries.values())[0]['rising']
#convert lists to dataframes
dftop = pd.DataFrame(top)
dfrising = pd.DataFrame(rising)
#join two data frames
joindfs = [dftop, dfrising]
allqueries = pd.concat(joindfs, axis=1)
#function to change duplicates
cols=pd.Series(allqueries.columns)
for dup in allqueries.columns[allqueries.columns.duplicated(keep=False)]:
cols[allqueries.columns.get_loc(dup)] = ([dup + '.' + str(d_idx)
if d_idx != 0
else dup
for d_idx in range(allqueries.columns.get_loc(dup).sum())]
)
allqueries.columns=cols
#rename to proper names
allqueries.rename({'query': 'top query', 'value': 'top query value', 'query.1': 'related query', 'value.1': 'related query value'}, axis=1, inplace=True)
#check your dataset
allqueries.head(50)
print(allqueries)
#save to csv
allqueries.to_csv('allqueries.csv')
#download from collab
from google.colab import files
#files.download("allqueries.csv")
How to Get Search Interest per region & Historical¶
In [10]:
#install pytrends
!pip install pytrends
#import the libraries
import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()
#provide your search terms
#kw_list=['Facebook', 'Apple', 'Amazon', 'Netflix', 'Google']
kw_list=['business intelligence', 'Data science', 'technology management', 'innovation', 'data governance']
#search interest per region
#run model for keywords (can also be competitors)
pytrend.build_payload(kw_list, timeframe='today 1-m')
# Interest by Region
regiondf = pytrend.interest_by_region()
#looking at rows where all values are not equal to 0
regiondf = regiondf[(regiondf != 0).all(1)]
#drop all rows that have null values in all columns
regiondf.dropna(how='all',axis=0, inplace=True)
#visualise
regiondf.plot(figsize=(20, 12), y=kw_list, kind ='bar')
Out[10]:
In [11]:
#historical interest
historicaldf = pytrend.get_historical_interest(kw_list, year_start=2020, month_start=10, day_start=1, hour_start=0, year_end=2021, month_end=10, day_end=1, hour_end=0, cat=0, geo='', gprop='', sleep=0)
#visualise
#plot a timeseries chart
historicaldf.plot(figsize=(20, 12))
#plot seperate graphs, using theprovided keywords
historicaldf.plot(subplots=True, figsize=(20, 12))
Out[11]:
