Pandas Seaborn

In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides, the power of pandas library dataframes for manipulating tabular data, and the great-looking statistical plots and analyses provided by the Seaborn library.

This tutorial assumes you're already familiar with some of the core concepts of HoloViews, which are explained in the other Tutorials .

In [1]:
import itertools

import numpy as np
import pandas as pd
import seaborn as sb
import holoviews as hv

np.random.seed(9221999)

We can now select static and animation backends:

In [2]:
hv.notebook_extension()
%output holomap='widgets' fig='svg'
HoloViewsJS successfully loaded in this cell.

Visualizing Distributions of Data

If import seaborn succeeds, HoloViews will provide a number of additional Element types, including Distribution , Bivariate , TimeSeries , Regression , and DFrame (a Seaborn -visualizable version of the DFrame Element class provided when only pandas is available).

We'll start by generating a number of Distribution Element s containing normal distributions with different means and standard deviations and overlaying them. Using the %%opts magic you can specify specific plot and style options as usual; here we deactivate the default histogram and shade the kernel density estimate:

In [3]:
%%opts Distribution (hist=False kde_kws=dict(shade=True))
d1 = 25 * np.random.randn(500) + 450
d2 = 45 * np.random.randn(500) + 540
d3 = 55 * np.random.randn(500) + 590
hv.Distribution(d1, label='Blue') *\
hv.Distribution(d2, label='Red') *\
hv.Distribution(d3, label='Yellow')
Out[3]:

Thanks to Seaborn you can choose to plot your distribution as histograms, kernel density estimates, and/or rug plots:

In [4]:
%%opts Distribution (rug=True kde_kws={'color':'indianred','linestyle':'--'})
hv.Distribution(np.random.randn(10), vdims=['Activity'])
Out[4]:

We can also visualize the same data with Bivariate distributions:

In [5]:
%%opts Bivariate (shade=True) Bivariate.A (cmap='Blues') Bivariate.B (cmap='Reds') Bivariate.C (cmap='Greens')
hv.Bivariate(np.array([d1, d2]).T, group='A') +\
hv.Bivariate(np.array([d1, d3]).T, group='B') +\
hv.Bivariate(np.array([d2, d3]).T, group='C')
Out[5]:

This plot type also has the option of enabling a joint plot with marginal distribution along each axis, and the kind option lets you control whether to visualize the distribution as a scatter , reg , resid , kde or hex plot:

In [6]:
%%opts Bivariate [joint=True] (kind='kde' cmap='Blues')
hv.Bivariate(np.array([d1, d2]).T, group='A')
Out[6]:

Working with TimeSeries data

Next let's take a look at the TimeSeries View type, which allows you to visualize statistical time-series data. TimeSeries data can take the form of a number of observations of some dependent variable at multiple timepoints. By controlling the plot and style option the data can be visualized in a number of ways, including confidence intervals, error bars, traces or scatter points.

Let's begin by defining a function to generate sine-wave time courses with varying phase and noise levels.

In [7]:
def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3, phase=0):
    x = np.linspace(0+phase, (n_x - 1) / 2+phase, n_x)
    y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)
    return y

Now we can create HoloMaps of sine and cosine curves with varying levels of observational and independent error.

In [8]:
sine_stack = hv.HoloMap(kdims=['Observation error','Random error'])
cos_stack = hv.HoloMap(kdims=['Observation error', 'Random error'])
for oe, te in itertools.product(np.linspace(0.5,2,4), np.linspace(0.5,2,4)):
    sines = np.array([sine_wave(31, oe, te) for _ in range(20)])
    sine_stack[(oe, te)] = hv.TimeSeries(sines, label='Sine', group='Activity',
                                         kdims=['Time', 'Observation'])
    cosines = np.array([sine_wave(31, oe, te, phase=np.pi) for _ in range(20)])
    cos_stack[(oe, te)]  = hv.TimeSeries(cosines, group='Activity',label='Cosine', 
                                         kdims=['Time', 'Observation'])

First let's visualize the sine stack with a confidence interval:

In [9]:
%%opts TimeSeries (ci=95 color='indianred')
sine_stack
Out[9]:

And the cosine stack with error bars:

In [10]:
%%opts TimeSeries (err_style='ci_bars')
cos_stack.last
Out[10]:

Since the %%opts cell magic has applied the style to each object individually, we can now overlay the two with different visualization styles in the same plot:

In [11]:
cos_stack.last * sine_stack.last
Out[11]:

Working with pandas DataFrames

In order to make this a little more interesting, we can use some of the real-world datasets provided with the Seaborn library. The holoviews DFrame object can be used to wrap the Seaborn-generated pandas dataframes like this:

In [12]:
iris = hv.DFrame(sb.load_dataset("iris"))
tips = hv.DFrame(sb.load_dataset("tips"))
titanic = hv.DFrame(sb.load_dataset("titanic"))
In [13]:
%output fig='png' dpi=100 size=150

Iris Data

Let's visualize the relationship between sepal length and width in the Iris flower dataset. Here we can make use of some of the inbuilt Seaborn plot types, starting with a pairplot that can plot each variable in a dataset against each other variable. We can customize this plot further by passing arguments via the style options, to define what plot types the pairplot will use and define the dimension to which we will apply the hue option.

In [14]:
%%opts DFrame (diag_kind='kde' kind='reg' hue='species')
iris.clone(label="Iris Data", plot_type='pairplot')
Out[14]:

When working with a DFrame object directly, you can select particular columns of your DFrame to visualize by supplying x and y parameters corresponding to the Dimension s or columns you want visualize. Here we'll visualize the sepal_width and sepal_length by species as a box plot and violin plot, respectively. By switching the x and y arguments we can draw either a vertical or horizontal plot.

In [15]:
%%opts DFrame [show_grid=False]
iris.clone(x='sepal_width', y='species', plot_type='boxplot') +\
iris.clone(x='species', y='sepal_width', plot_type='violinplot')
Out[15]:

Titanic passenger data

The Titanic passenger data is a truly large dataset, so we can make use of some of the more advanced features of Seaborn and pandas. Above we saw the usage of a pairgrid , which allows you to quickly compare each variable in your dataset. HoloViews also support Seaborn based FacetGrids . The FacetGrid specification is simply passed via the style options, where the map keyword should be supplied as a tuple of the plotting function to use and the Dimension s to place on the x axis and y axis. You may also specify the Dimension s to lay out along the row s and col umns of the plot, and the hue groups:

In [16]:
%%opts DFrame (map=('barplot', 'alive', 'age') col='class' row='sex' hue='pclass' aspect=1.0)
titanic.clone(plot_type='facetgrid')
Out[16]:

FacetGrids support most Seaborn and matplotlib plot types:

In [17]:
%%opts DFrame (map=('regplot', 'age', 'fare') col='class' hue='class')
titanic.clone(plot_type='facetgrid')
Out[17]: