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.

Customizing Ticks

Matplotlib’s default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot.

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Text and Annotation

Creating a good visualization involves guiding the reader so that the figure tells a story. In some cases, this story can be told in an

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Multiple Subplots

Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has the concept of subplots: groups of smaller axes

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Customizing Colorbars

Plot legends identify discrete labels of discrete points. For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be

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Customizing Plot Legends

Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We previously saw how to create a simple legend; here we’ll

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Density and Contour Plots

Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions. There are three Matplotlib functions that can be helpful

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Visualizing Errors

For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself. For example,

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Simple Scatter Plots

Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. Instead of points being joined by line segments,

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Simple Line Plots

Perhaps the simplest of all plots is the visualization of a single function y=f(x)y=f(x). Here we will take a first look at creating a simple plot

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Further Resources

In this chapter, we’ve covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To

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Working with Time Series

Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with

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Vectorized String Operations

One strength of Python is its relative ease in handling and manipulating string data. Pandas builds on this and provides a comprehensive set of vectorized string

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Pivot Tables

We have seen how the GroupBy abstraction lets us explore relationships within a dataset. A pivot table is a similar operation that is commonly seen in spreadsheets and other

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Aggregation and Grouping

An essential piece of analysis of large data is efficient summarization: computing aggregations like sum(), mean(), median(), min(), and max(), in which a single number gives insight into the nature

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Hierarchical Indexing

Up to this point we’ve been focused primarily on one-dimensional and two-dimensional data, stored in Pandas Series and DataFrame objects, respectively. Often it is useful to go beyond this

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Handling Missing Data

The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous. In particular,

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