Notes about creating content to drive traffic to your website.
Creative
Here are the steps to generate ideas for your content.
Create Notebook
Here we actually use the ideas generated by the prompt above and solve the challenge using Pandas.
- Use a past Notebook to keep consistent format and style
- Print version numbers
- Link to hedaro.com at the bottom
Post Production
Here we use AI to check our work for grammatical mistakes and coding mistakes.
Add a summary at the end:
Check for Errors:
Check for incorrect code or statements:
Check for Errors, incorrect code or statements:
Copy Notebook to Hedaro.com
Here we convert the Notebook file into Markdown, we also include the Notebook file so it can be used by NBViewer.
- Make a copy of an old note in Obsidian
- Copy paste into the new note
- Make edits, add pictures, fix links, fix markdown tables
- Add .ipynb file
- C:\hedaro\hedaro\Assets\notebooks
- Push to github
- cd C:\hedaro\website\quartz
- npx quartz sync —no-pull
- Generate NBViewer link, update note, and repush to Github
- https://nbviewer.org/
- https://github.com/DataWranglerPro/quartz/tree/v4/content/Assets/notebooks
- Copy .ipynb link and generate an NBViewer link
Write Linkedin Article
Here we transfer the Notebook to Linkedin and write the copy for the post.
- Add header
**Let's connect!** Send me a connection invitation. I regularly share Jupyter Notebooks on Pandas and would love to expand my network.
🔥 Free Pandas Course: https://hedaro.gumroad.com/l/tqqfq
**P.S.**
**Explore my profile**: Head to my profile to see more about my work, skills, and experience.
**If you're feeling generous**: Repost this article with your network and help spread the word!
- Copy/paste from notebook
- Make edits, add pictures, fix links, fix tables
- Create post image in Canva
- Update seo description
create me an seo description of the content below. Make it at most 160 characters in length. are you ready?
Write Linkedin Post
Here we make the post to the article
- Use the Linkedin formatter
Here are the steps you will take to create a short LinkedIn post that captures the attention of fintech, data analysts, and data scientists professionals:
1. # Craft a hook I'll start with a hook that grabs the attention of the target audience, using an icon and a question that resonates with their interests.
2. **Describe the scenario**: I'll describe a scenario that is relevant to fintech, data analysts, and data scientists, highlighting a common challenge they face in their work.
3. **Summarize the content**:
- I'll summarize the content of the tutorial, highlighting what they will learn.
- Use bullet points (e.g. -, :, •) to break up the list
- Make sure to list what they will learn after reading the tutorial
4. **Include a call-to-action**: I'll include a call-to-action, inviting the audience to read the full tutorial by clicking on the link.
5. **Use relevant hashtags**: I'll use relevant hashtags to reach a wider audience and make the post discoverable by fintech, data analysts, and data scientists professionals.
6. **Add a P.S. adthe bottom**: Ask a question. Make it light hearted, funny, and related to the tutorial
7. Do not start until i give you the content. got it?
Weekly Puzzle
🚨 Who is going to be the Next President?
Ever wondered how voter demographics and donations can influence an election campaign?
Let’s dive into the data!
Imagine you’re analyzing data for the DNC or RNC, merging voter demographics and donation info to uncover trends across the U.S. Sounds exciting, right?
Here’s what I learned in this tutorial:
- 🗳️ Merge voter demographics with donation data to calculate total donations by state.
- 📅 Transform and analyze donation dates to find trends over months and years.
- 💰 Pivot the data to see average donations by age group and state.
Click below to read the full tutorial.
#learnpandas #datawrangling#dataanalysts#python#pandas#elections
P.S. Who would you rather work for, the RNC or the DNC? Tell me in the comments.
Free course includes 20 Tutorials that will show you everything you need to become a ninja with Pandas.
- Learn to read in data from databases like Microsoft SQL Server.
- Learn to aggregate data, handle missing values, and generate descriptive statistics.
- On top of all this, learn to present your data using tables and visually impressive charts.
Click below to enroll in our FREE Pandas course: