Data Science och AI guider för doktorander

Här följer ett par guider för dig som behöver lära dig AI. Guiderna tillhandahålls kostnadsfritt av forskare vid Chalmers & GU och finns för att stötta doktorander.

Data Science och AI guider för doktorander

Här följer ett par guider för dig som behöver lära dig AI. Guiderna tillhandahålls kostnadsfritt av forskare vid Chalmers & GU och finns för att stötta doktorander.

Operating on Data in Pandas

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with

Läs »

Data Indexing and Selection

In Chapter 2, we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. These included indexing (e.g., arr[2, 1]), slicing

Läs »

Sorting Arrays

Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy. This section covers algorithms related

Läs »

Fancy Indexing

In the previous sections, we saw how to access and modify portions of arrays using simple indices (e.g., arr[0]), slices (e.g., arr[:5]), and Boolean masks (e.g., arr[arr >

Läs »

The Basics of NumPy Arrays

Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas (Chapter 3) are built around the NumPy array. This

Läs »

More IPython Resources

In this chapter, we’ve just scratched the surface of using IPython to enable data science tasks. Much more information is available both in print and

Läs »

Profiling and Timing Code

In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations. Early in developing your

Läs »

Errors and Debugging

Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process. This section will briefly

Läs »

IPython and Shell Commands

When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and

Läs »

Input and Output History

Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.

Läs »

IPython Magic Commands

The previous two sections showed how IPython lets you use and explore Python efficiently and interactively. Here we’ll begin discussing some of the enhancements that

Läs »