Notes about creating content to drive traffic to your website.


Here are the steps to generate ideas for your content.

fintech|data scientist|data analyst professionals
Here are the instructions you will follow to generate a random Python pandas challenge for data analyst professionals:
Challenge Generation Instructions
Challenge Description: Write a clear and concise description of the challenge, including the problem statement, make it sound like a story you are telling a friend.
Tasks: Create 3 tasks that identify specific pain points that pandas solves.
Data Generation: Write code to generate a sample dataset that represents a real-world scenario, using libraries like numpy and pandas.
Data Size: Ensure the dataset is large enough to require efficient pandas operations, but not so large that it becomes unwieldy.
Complexity: Design the challenge to require intermediate to advanced pandas skills.
Summary of data: Provide a description of the dataset and column definitions

also please avoid these topics:
Movie Madness
E-commerce Sales Analysis
Energy consumption
Flight Frenzy
Saving the Music Festival
Financial Fiasco: A Bank's Dilemma

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 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:

Can you summarize the Pandas tutorial below and provide in bullets what the reader learned. Are you ready?

Check for Errors:

look for grammatical mistakes in the content below. tell me the issues found but do not rewrite my content. Are you ready?

Check for incorrect code or statements:

Look for any code errors or incorrect statements in the content below. tell me the issues found but do not rewrite my content. Are you ready?

Check for Errors, incorrect code or statements:

identify any:

- Grammatical mistakes
- Code errors (if the content contains code snippets)
- Incorrect statements (logical or factual errors)

List out the issues without rewriting my content. Are you ready?

Copy Notebook to

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
    • 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.


**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

Write me a short linkedin post that captures peoples attention using the links below. Add icons.

Here is an example you can try to follow when creating the posts:

Duplicates detected! But what's the real story behind the numbers?

🚨 Data scientists, here is the scenario:

As a data scientist in an e-commerce company, you are tasked with identifying and combining duplicate customer records from various sources. The goal is to calculate the total spend for each unique customer.

Summary of Content:
- Identify duplicate customer records based on CustomerID, Name, and Email
- Combine duplicates into a single row, summing up the values in the Spent column
- Calculate total spend for each unique customer using pandas' groupby and sum functions

Bonus question: What's the average spend per customer for the top 3 customers with the highest total spend? (Be the first to answer in the comments!)

Read the full tutorial by clicking the picture below.

#Pandas #DataAnalysis #Python #TechTips 

make the content specifically targeted for: fintech, data analysts, data scientists professionals, this is my avatar

do not start until i give you the content. got it?
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. Do not start until i give you the content. got it?

Weekly Puzzle

- Create a short Pandas puzzle consisting of a few lines of code and a question.
- make the code simple enough so that is can be solved in a person's head
- Choose randomly from the styles below:
1. **What is the output?**: Given a code snippet, predict the output or result.
2. **Fill in the missing code**: Complete a partially written code snippet to achieve a specific task.
3. **Find the error in the code**: Identify and correct the error in a given code snippet.
4. **Write a code snippet to achieve a task**: Write a code snippet from scratch to accomplish a specific task.
5. **Explain the code**: Explain how a given code snippet works or what it does.
6. **Optimize the code**: Suggest improvements or optimizations for a given code snippet.
7. **What is the purpose of the code?**: Identify the purpose or functionality of a given code snippet.
8. **True or False**: Determine whether a given statement about a code snippet is true or false.
9. **Multiple Choice**: Choose the correct answer from a set of options for a given question about a code snippet.
10. **Debug the code**: Fix the errors in a given code snippet to make it work correctly.
- Create a short Pandas puzzle consisting of a few lines of code and a question.
- Draft a LinkedIn post for the puzzle, including:
    - A hook to capture attention.
    - The puzzle code.
    - The question.
    - A P.S. at the bottom mentioning the reward for the first correct answer (a free Pandas course or a 15-minute 1:1 call with you).
- make the code simple enough so that is can be solved in a person's head

💸 Unlock the secrets of real estate investing!

Are you a fintech professional looking to uncover investment opportunities in the real estate market?

In this tutorial, you’ll learn to:

  • Extract the quarter and year from the ‘Date’ column
  • Calculate the return on investment (ROI) for each property
  • Group data by region and property type to calculate total sales, average sale price, and average ROI
  • Create a dashboard with three charts to visualize top-performing regions and property types

#fintech #datawrangling#dataanalysts#python#pandas

P.S. Read the full tutorial and start uncovering investment opportunities in the real estate market!

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: