NumPy Cheat Sheet for Data Scientists

TODAY’S DAILY DOSE OF DATA SCIENCE

NumPy Cheat Sheet for Data Scientists

Here’s a NumPy cheat sheet that depicts the 40 most commonly used methods from NumPy:

In our experience, you will use these methods 95% of the time working with NumPy.

Whenever you are trying to learn a new library, mastering/practicing each and every method is not necessary.

Instead, put Pareto’s principle to work—20% of your inputs contribute towards generating 80% of your outputs.

👉 Over to you: Have I missed any commonly used methods?

IN CASE YOU MISSED IT

​​Graph Neural Networks​

• Google Maps uses graph ML for ETA prediction.
• Pinterest uses graph ML (PingSage) for recommendations.
• Netflix uses graph ML (SemanticGNN) for recommendations.
• Spotify uses graph ML (HGNNs) for audiobook recommendations.
• Uber Eats uses graph ML (a GraphSAGE variant) to suggest dishes, restaurants, etc.

The list could go on since almost every major tech company I know employs graph ML in some capacity.

Becoming proficient in ​graph ML​ now seems to be far more critical than traditional deep learning to differentiate your profile and aim for these positions.

A significant proportion of our real-world data often exists (or can be represented) as graphs:

• Entities (nodes) are connected by relationships (edges).
• Connections carry significant meaning, which, if we knew how to model, can lead to much more robust models.

The field of ​graph neural networks (GNNs)​ intends to fill this gap by extending deep learning techniques to graph data.

Learn sophisticated graph architectures and how to train them on graph data in ​this crash course​​ →

In case you missed it

Building an MCP server

Imagine you only know English.

To get info from a person who only knows:

• French, you must learn French.
• German, you must learn German.
• And so on.

Learning even 5 languages will be a nightmare for you!

But what if you add a translator that understands all languages?

• You talk to the translator.
• It infers the info you want.
• It picks the person to talk to.
• It gets you a response.

The translator is like an MCP!

It lets you (Agents) talk to other people (tools) through a single interface.

​We recently covered how you can build your MCP servers here →​

Here's what we did:

• Understood MCP with a simple analogy.
• Built a local MCP server and interact with it via Cursor IDE.
• Integrated ​​Firecrawl’s MCP server​​ and interacted with its tools.