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"))

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]: