Data visualization in Python

Data Visualization in Python
Marc Garcia - @datapythonista
Data Visualisation Summit - London, 2017
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Data Visualization in Python - @datapythonista

About me
http://datapythonista.github.io
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Python for data science
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Why Python?
Python is the favorite of many:
Fast to write: Batteries included
Easy to read: Readability is KEY
Excellent community: Conferences, local groups, stackoverﬂow...
Ubiquitous: Present in all major platforms
Easy to integrate: Implements main protocols and formats
Easy to extend: C extensions for low-level operations
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Python performance
Is Python fast for data science?
Short answer: No
Long answer: Yes
numpy
Cython
C extensions
Numba
etc.
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Python is great for data science
A whole ecosystem exists:
numpy
scipy
pandas
statsmodels
scikit-learn
etc.
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Python environment
One ring to rule them all:
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Python platform
Jupyter notebook
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Python for visualization
Main libraries:
Matplotlib
Seaborn
Bokeh
HoloViews
Datashader
Domain-speciﬁc
Folium: maps
yt: volumetric data
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Visualization tools
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Visualization tools
Matplotlib
First Python visualization tool
Still a de-facto standard
Replicates Matlab API
Supports many backends
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Visualization tools
Matplotlib
import numpy
from matplotlib import pyplot
x = numpy.linspace(0., 100., 1001)
y = x + numpy.random.randn(1001) * 5
pyplot.plot(x, y)
pyplot.xlabel(’time (seconds)’)
pyplot.ylabel(’some noisy signal’)
pyplot.title(’A simple plot in matplotlib’)
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Visualization tools
Matplotlib
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Visualization tools
Matplotlib
import numpy
x = numpy.linspace(0., 100., 1001)
y1 = x + numpy.random.randn(1001) * 3
y2 = 45 + x * .4 + numpy.random.randn(1001) * 7
pyplot.plot(x, y1, label=’Our previous signal’)
pyplot.plot(x, y2, color=’orange’, label=’A new signal’)
pyplot.xlabel(’time (seconds)’)
pyplot.ylabel(’some noisy signal’)
pyplot.title(’A simple plot in matplotlib’)
pyplot.legend()
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Visualization tools
Matplotlib
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Visualization tools
Seaborn
Matplotlib wrapper
Built-in themes
Higher level plots:
Heatmap
Violin plot
Pair plot
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Visualization tools
Seaborn
import seaborn
flights_flat = seaborn.load_dataset(’flights’)
flights = flights_flat.pivot(’month’, ’year’, ’passengers’)
seaborn.heatmap(flights, annot=True, fmt=’d’)
pyplot.title(’Number of flight passengers (thousands)’)
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Visualization tools
Seaborn
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Visualization tools
Bokeh
Client-server architecture: JavaScript front-end
Interactive
Drawing shapes to generate plots
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Visualization tools
Bokeh
Demo
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Visualization tools
HoloViews
Bokeh wrapper
Higher level plots
Mainly for Bokeh, but other backends supported
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Visualization tools
HoloViews
import numpy as np
import holoviews as hv
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
hv.extension(’bokeh’)
counties = {code: county for code, county in counties.items() if county[’state’] == ’tx’}
county_xs = [county[’lons’] for county in counties.values()]
county_ys = [county[’lats’] for county in counties.values()]
county_names = [county[’name’] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
county_polys = {name: hv.Polygons((xs, ys), level=rate, vdims=[’Unemployment’])
for name, xs, ys, rate in zip(county_names, county_xs, county_ys,
county_rates)}
choropleth = hv.NdOverlay(county_polys, kdims=[’County’])
plot_opts = dict(logz=True, tools=[’hover’], xaxis=None, yaxis=None,
show_grid=False, show_frame=False, width=500, height=500)
style = dict(line_color=’white’)
choropleth({’Polygons’: {’style’: style, ’plot’: plot_opts}})
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Visualization tools
HoloViews
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Visualization tools
Datashader
Bokeh wrapper
Built for big data
Advanced subsampling and binning techniques
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Visualization tools
Datashader
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Visualization tools
Folium
Visualization of maps
Compatible with Google maps and Open street maps
Visualization of markers, paths and polygons
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Visualization tools
Folium
import folium
m = folium.Map(location=[45.372, -121.6972],
zoom_start=12,
tiles=’Stamen Terrain’)
folium.Marker(location=[45.3288, -121.6625],
popup=’Mt. Hood Meadows’,
icon=folium.Icon(icon=’cloud’)).add_to(m)
popup=’Timberline Lodge’,
icon=folium.Icon(color=’green’)).add_to(m)
popup=’Some Other Location’,
icon=folium.Icon(color=’red’, icon=’info-sign’)).add_to(m)
m
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Visualization tools
Folium
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Visualization tools
yt
Visualization of volumetric data
Compatible with many formats
Projects multidimensional data to a 2-D plane
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Visualization tools
yt
import yt
ds = yt.load(’MOOSE_sample_data/out.e-s010’)
sc = yt.create_scene(ds)
ms = sc.get_source()
ms.cmap = ’Eos A’
cam = sc.camera
cam.focus = ds.arr([0.0, 0.0, 0.0], ’code_length’)
cam_pos = ds.arr([-3.0, 3.0, -3.0], ’code_length’)
north_vector = ds.arr([0.0, -1.0, -1.0], ’dimensionless’)
cam.set_position(cam_pos, north_vector)
cam.resolution = (800, 800)
sc.save()
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Visualization tools
yt
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Conclusions
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Conclusions
Conclusions
Python is great as a programming language
And is great for data science
Plenty of options for visualization:
Standard plots
Ad-hoc plots
Interactive
3D plots
Maps
Big data
Specialized
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Conclusions
Questions?
@datapythonista
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Data visualization in Python

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