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Joint plot

Table of Contents

  1. Default Presentation of Joint Plot

  2. Change Geom Types

  3. Geometries Customization

  4. Marginal Layers Customization

  5. Grouping

  6. Additional Layer

In [1]:
import pandas as pd

from lets_plot.bistro import *
from lets_plot import *
In [2]:
LetsPlot.setup_html()
In [3]:
df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/iris.csv")
print(df.shape)
df.head()

(150, 5)
Out[3]:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa

1. Default Presentation of Joint Plot

In the simplest case, assign x and y to create a scatterplot (using geom_point()) with marginal histograms (using geom_histogram()).

In [4]:
joint_plot(df, "petal_length", "petal_width")
Out[4]:

2. Change Geom Types

Besides the points there are another two types of geoms: tile and density2d(f).

In [5]:
joint_plot(df, "petal_length", "petal_width", geom='tile')
Out[5]:
In [6]:
joint_plot(df.dropna(), "petal_length", "petal_width", color_by="species", geom='density2d')
Out[6]:

3. Change Geom Parameters

Use additional parameters for better customization: color, size, alpha, etc.

In [7]:
joint_plot(df, "petal_length", "petal_width", color="#756bb1", size=8, alpha=.5, se=False)
Out[7]:

4. Marginal Layers Customization

marginal parameter is a shortcut for the ggmarginal() layer.

In [8]:
joint_plot(df, "petal_length", "petal_width", color="black", marginal="box:lb:.03,hist:t:.4,hist:r") + \
    ggmarginal("tr", layer=geom_area(stat='density', color="magenta", fill="magenta", alpha=.1)) + \
    theme(axis_line_x='blank', axis_line_y='blank')
Out[8]:

5. Grouping

The color_by parameter sets the mapping to the fill and color aesthetics.

In [9]:
joint_plot(df, "petal_length", "petal_width", color_by="species", marginal="hist:tr")
Out[9]:

6. Additional Layer

Add any other layer that supports x and y aesthetics (e.g. points layer with the geom_point() function).

In [10]:
joint_plot(df, "petal_length", "petal_width", geom='density2df', \
           color="#993404", alpha=1/3, reg_line=False) + \
    geom_point(size=5, shape=21, color="#993404", fill="#ffffd4") + \
    scale_fill_gradient(low="#d95f0e", high="#fff7bc")
Out[10]: