Keywords from Google Search Console to list based on frequency of occurence using Pandas

From inside the Google Analytics GA4 report, download the CSV ( Comma Separated Value ) file

Google organic search traffic: Landing page + query string
CSV file structure

We can skip the top rows and manage our own column headers. While creating the DataFrame from CSV file we will use the options skiprows, usecols,index_col and names to customize the DataFrame as per our requirement.
While using usecols we have given 6 columns ( 0 indexed ) starting from first column as 0. To match these 6 columns we have used a list of column headers l_headers based on our required headers.

l_headers=['page','clicks','imp','ctr','position','tm'] # Changed the name of the columns 
df=pd.read_csv(path,skiprows=11,usecols=[0,1,2,3,4,8],index_col=0,names=l_headers)

After creating the DataFrame df we are using the same to create a SQLite table to store them using to_sql(). We can use MySQL also to store the DataFrame.

import pandas as pd
#path and name of downloaded csv file. 
path='C:\\testing\\analytics\\data\\y23_04.csv'
table_name='y23_04' # change name of the SQlite table 
####### No edit ######
l_headers=['page','clicks','imp','ctr','position','tm'] # Changed the name of the columns 
df=pd.read_csv(path,skiprows=11,usecols=[0,1,2,3,4,8],index_col=0,names=l_headers) 
df['position']=df['position']. round(2)
df['ctr']=df['ctr']. round(2)
df['tm']=df['tm']. round(2)
print(df.head())

## Inserted the DataFrame to SQLite database 
from sqlalchemy import create_engine
my_conn = create_engine("sqlite:///C:\\my_folder\\analytics3.db")
my_conn=my_conn.connect()
df.to_sql(con=my_conn,name=table_name,if_exists='replace')

Frequency of key words

Download keyword list from the Google Search Console in CSV format. Set the number of rows per page towards the end of the page and then change the order of Position based on lowest to highest. Now the heighest position will be at the top. Download the CSV file to your local system.

Read the csv file by using read_csv(), by using the column Top Queries we will create one new dataframe (df2).
While creating the dataframe we will use str to create string, then split the string and use stack. Finally we will use value_count() to create a list of key words with the frequency of their occurrence.

We are storing the final key word list with the frequency of occurrence using CSV file using to_csv().
This gives us the list of words which are as per google have relatively better authoritativeness ( Part of E-A-T : expertise authoritativeness and trustworthiness).
Google search Console Keywords in CSV format

Google search Console Keywords in CSV format

import pandas as pd  # importing pandas
df = pd.read_csv("H:\\data\\seo\\query-rank.csv")  # reading csv file
df2 = df["Top queries"].str.split(expand=True).stack().value_counts()
print(df2.head(15)) # printing top 15 keywords
df2.to_csv("H:\\data\\seo\\to_words.csv") # saving to csv file

Pandas contains() split() slice()

Numpy arrays Python & MySQL Python- Tutorials

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Frequency of key words

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