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  3. Intermediate Data Visualization with Seaborn
  • 1

    Seaborn Introduction

    Kostenlos

    Introduction to the Seaborn library and where it fits in the Python visualization landscape.

  • 2

    Customizing Seaborn Plots

    Overview of functions for customizing the display of Seaborn plots.

  • 3

    Additional Plot Types

    Overview of more complex plot types included in Seaborn.

  • 4

    Creating Plots on Data Aware Grids

    Using Seaborn to draw multiple plots in a single figure.

Initializing

Ubung

Building a JointGrid and jointplot

Seaborn's JointGrid combines univariate plots such as histograms, rug plots and kde plots with bivariate plots such as scatter and regression plots. The process for creating these plots should be familiar to you now. These plots also demonstrate how Seaborn provides convenient functions to combine multiple plots together.

For these exercises, we will use the bike share data that we reviewed earlier. In this exercise, we will look at the relationship between humidity levels and total rentals to see if there is an interesting relationship we might want to explore later.

Anweisungen 1/2

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  • Use Seaborn's "whitegrid" style for these plots.
  • Create a JointGrid() with "hum" on the x-axis and "total_rentals" on the y.
  • Plot a regplot() and histplot() on the margins.