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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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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/85/Data-visualization-in-Python-22-320.jpg)
![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)
folium.Marker(location=[45.3311, -121.7113],
popup=’Timberline Lodge’,
icon=folium.Icon(color=’green’)).add_to(m)
folium.Marker(location=[45.3300, -121.6823],
popup=’Some Other Location’,
icon=folium.Icon(color=’red’, icon=’info-sign’)).add_to(m)
m
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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/85/Data-visualization-in-Python-27-320.jpg)
![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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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/85/Data-visualization-in-Python-30-320.jpg)
![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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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/75/Data-visualization-in-Python-22-2048.jpg)
![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)
folium.Marker(location=[45.3311, -121.7113],
popup=’Timberline Lodge’,
icon=folium.Icon(color=’green’)).add_to(m)
folium.Marker(location=[45.3300, -121.6823],
popup=’Some Other Location’,
icon=folium.Icon(color=’red’, icon=’info-sign’)).add_to(m)
m
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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/75/Data-visualization-in-Python-27-2048.jpg)
![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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Data Visualization in Python - @datapythonista](https://image.slidesharecdn.com/datavisualizationinpythonv2-171103225436/75/Data-visualization-in-Python-30-2048.jpg)
Uploaded byMarc Garcia
Data visualization in Python
This document discusses data visualization tools in Python. It introduces Matplotlib as the first and still standard Python visualization tool. It also covers Seaborn which builds on Matplotlib, Bokeh for interactive visualizations, HoloViews as a higher-level wrapper for Bokeh, and Datashader for big data visualization. Additional tools discussed include Folium for maps, and yt for volumetric data visualization. The document concludes that Python is well-suited for data science and visualization with many options available.
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Data visualization in Python
- 1. Data Visualization inPython Marc Garcia - @datapythonista Data Visualisation Summit - London, 2017 1 / 34 Data Visualization in Python - @datapythonista
- 2. About me http://datapythonista.github.io 2 /34 Data Visualization in Python - @datapythonista
- 3. Python for datascience 3 / 34 Data Visualization in Python - @datapythonista
- 4. Python for datascience 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 4 / 34 Data Visualization in Python - @datapythonista
- 5. Python for datascience Python performance Is Python fast for data science? Short answer: No Long answer: Yes numpy Cython C extensions Numba etc. 5 / 34 Data Visualization in Python - @datapythonista
- 6. Python for datascience Python is great for data science A whole ecosystem exists: numpy scipy pandas statsmodels scikit-learn etc. 6 / 34 Data Visualization in Python - @datapythonista
- 7. Python for datascience Python environment One ring to rule them all: 7 / 34 Data Visualization in Python - @datapythonista
- 8. Python for datascience Python platform Jupyter notebook 8 / 34 Data Visualization in Python - @datapythonista
- 9. Python for datascience Python for visualization Main libraries: Matplotlib Seaborn Bokeh HoloViews Datashader Domain-specific Folium: maps yt: volumetric data 9 / 34 Data Visualization in Python - @datapythonista
- 10. Visualization tools 10 /34 Data Visualization in Python - @datapythonista
- 11. Visualization tools Matplotlib First Pythonvisualization tool Still a de-facto standard Replicates Matlab API Supports many backends 11 / 34 Data Visualization in Python - @datapythonista
- 12. Visualization tools Matplotlib import numpy frommatplotlib 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’) 12 / 34 Data Visualization in Python - @datapythonista
- 13. Visualization tools Matplotlib 13 /34 Data Visualization in Python - @datapythonista
- 14. Visualization tools Matplotlib import numpy frommatplotlib import pyplot 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() 14 / 34 Data Visualization in Python - @datapythonista
- 15. Visualization tools Matplotlib 15 /34 Data Visualization in Python - @datapythonista
- 16. Visualization tools Seaborn Matplotlib wrapper Built-inthemes Higher level plots: Heatmap Violin plot Pair plot 16 / 34 Data Visualization in Python - @datapythonista
- 17. Visualization tools Seaborn from matplotlibimport pyplot 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)’) 17 / 34 Data Visualization in Python - @datapythonista
- 18. Visualization tools Seaborn 18 /34 Data Visualization in Python - @datapythonista
- 19. Visualization tools Bokeh Client-server architecture:JavaScript front-end Interactive Drawing shapes to generate plots 19 / 34 Data Visualization in Python - @datapythonista
- 20. Visualization tools Bokeh Demo 20 /34 Data Visualization in Python - @datapythonista
- 21. Visualization tools HoloViews Bokeh wrapper Higherlevel plots Mainly for Bokeh, but other backends supported 21 / 34 Data Visualization in Python - @datapythonista
- 22. Visualization tools HoloViews import numpyas 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}}) 22 / 34 Data Visualization in Python - @datapythonista
- 23. Visualization tools HoloViews 23 /34 Data Visualization in Python - @datapythonista
- 24. Visualization tools Datashader Bokeh wrapper Builtfor big data Advanced subsampling and binning techniques 24 / 34 Data Visualization in Python - @datapythonista
- 25. Visualization tools Datashader 25 /34 Data Visualization in Python - @datapythonista
- 26. Visualization tools Folium Visualization ofmaps Compatible with Google maps and Open street maps Visualization of markers, paths and polygons 26 / 34 Data Visualization in Python - @datapythonista
- 27. 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) folium.Marker(location=[45.3311, -121.7113], popup=’Timberline Lodge’, icon=folium.Icon(color=’green’)).add_to(m) folium.Marker(location=[45.3300, -121.6823], popup=’Some Other Location’, icon=folium.Icon(color=’red’, icon=’info-sign’)).add_to(m) m 27 / 34 Data Visualization in Python - @datapythonista
- 28. Visualization tools Folium 28 /34 Data Visualization in Python - @datapythonista
- 29. Visualization tools yt Visualization ofvolumetric data Compatible with many formats Projects multidimensional data to a 2-D plane 29 / 34 Data Visualization in Python - @datapythonista
- 30. 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() 30 / 34 Data Visualization in Python - @datapythonista
- 31. Visualization tools yt 31 /34 Data Visualization in Python - @datapythonista
- 32. Conclusions 32 / 34 DataVisualization in Python - @datapythonista
- 33. Conclusions Conclusions Python is greatas 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 33 / 34 Data Visualization in Python - @datapythonista
- 34. Conclusions Questions? @datapythonista 34 / 34 DataVisualization in Python - @datapythonista