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Fetching 25K+ Rows of GSC Data using GSC API🚀 with Python🐍

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After watching this video you will be able to access more than 25K rows of data from Google Search Console. Then you could build a data frame, and export the data to CSV or Excel. You will also learn how to user Dimensions and Dimension Filters in GSC API. 💯
💡Timeline:
00:00 Thank you for the positive response and previous feedback.
00:28 Connecting on Social
02:10 Addressing the feedback
03:45 Project Page & Comment Section for GSC API x Python
05:38 Recap of Previous Video
💡Simplifying the previous code:
06:10 Starting of Explanation of 2nd Video
07:58 Consolidating & Simplifying the code from 1st Video
09:26 Shoutout to Rakesh Patel
10:30 Accessing GSC Data using the single API call
💡Writing While Loop for 25K+ rows of Data
12:26 Challenge of Accessing more than 25K+ rows of data
12:50 Python Operators, Conditions, String, List, Dictionary, For Loop, While Loop
15:26 Logic of While Loop to fetch 25K+ rows using multiple API calls.
💡Using Pandas & Building Dataframe:
25:24 How to Build a Dataframe using Pandas?
34:44 Export to CSV or Excel
💡Using Dimensions and Dimension Filters:
36:44 Using Dimensions
39:44 Using Dimensions Filters
Using Dimension Filters
43:54 Closing Remarks
💡Timeline:
00:00 Thank you for the positive response and previous feedback.
00:28 Connecting on Social
02:10 Addressing the feedback
03:45 Project Page & Comment Section for GSC API x Python
05:38 Recap of Previous Video
💡Simplifying the previous code:
06:10 Starting of Explanation of 2nd Video
07:58 Consolidating & Simplifying the code from 1st Video
09:26 Shoutout to Rakesh Patel
10:30 Accessing GSC Data using the single API call
💡Writing While Loop for 25K+ rows of Data
12:26 Challenge of Accessing more than 25K+ rows of data
12:50 Python Operators, Conditions, String, List, Dictionary, For Loop, While Loop
15:26 Logic of While Loop to fetch 25K+ rows using multiple API calls.
💡Using Pandas & Building Dataframe:
25:24 How to Build a Dataframe using Pandas?
34:44 Export to CSV or Excel
💡Using Dimensions and Dimension Filters:
36:44 Using Dimensions
39:44 Using Dimensions Filters
Using Dimension Filters
43:54 Closing Remarks
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