Visualize Branded vs Non Branded Queries 🚀 using GSC API & 🐍 Python

Learn how to plot charts for Branded and Non-Branded Queries over time. Uncover unique insights like No. of Unique Queries ranking for Brand vs Non-Brand Queries. Also explore the metrics like Clicks, Impressions, CTR and Position.
✅ Github Code Link:
github.com/TheMihirNaik/googl...
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LinkedIn : / mihir23192
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Link to this Playlist : • Google Search Console ...
✅Google Search Console API Overview: developers.google.com/webmast...
✅Plotly Express in Python:
plotly.com/python/plotly-expr...
✅Pandas:
pandas.pydata.org/
💡Timeline:
00:00 Recap of what's been done so far
00:48 Overview of this video
02:18 Why is this visualization important?
03:48 Thanks for the comments on LinkedIn Post
05:20 Call for Feedback and Engagement
💡Preparing DataFrame:
05:32 Creating a New Google Colab Notebook
06:22 Authenticating GSC Service
06:36 Getting a List of Websites
06:58 Selecting Dates, Dimensions & Fetching Rows
08:02 Building Dataframe
💡Preparing DataFrame for Branded vs Non-Branded Queries
08:38 Brief Explanation of Tagging Queries by Branded vs Non-branded
11:20 Brief Explanation of Python Functions
13:24 Creating a Function that will determine the Keyword Type
16:06 Creating a new Column "keyword_type" that will tag branded vs non-branded
19:14 Creating a DataFrame grouped by Date and Keyword_Type
💡Plotting Charts using Dataframe
21:28 Plotting the Chart showing Clicks by Branded vs Non-Branded
23:04 Plotting the Chart showing Impressions by Branded vs Non-Branded
23:28 Plotting the Chart showing CTR by Branded vs Non-Branded
23:44 Plotting the Chart showing Position by Branded vs Non-Branded
24:26 Plotting the Chart showing the Count of Queries by Branded vs Non-Branded
💡Closing Remarks
25:56 What's up and coming?