The document summarizes Dr. Margriet Groenendijk's presentation at PyCon UK on navigating data visualization with Python. It discusses using various Python packages like matplotlib, seaborn, and PixieDust to load, analyze, and visualize data in Jupyter notebooks. PixieDust allows loading data from various sources and creating interactive dashboards. The presentation recommends resources for further learning.

1. Dr. Margriet Groenendijk | IBM
PyCon UK | 15 September 2018 | Cardiff
Navigating the Magical Data
Visualisation Forest

2. http://ibm.biz/slides-margriet
Navigating the Magical Data
Visualisation Forest

3. Data Visualisation
@MargrietGr

4. Data Visualisation
with Python
@MargrietGr

5. Data Visualisation
with Python
in a Jupyter
notebook
@MargrietGr

6. Jupyter notebooks
Local
Linux, Mac, Windows
Anaconda
SaaS
Google
Microsoft
Amazon
IBM
and others
@MargrietGr

7. DOC ID / Month XX, 2018 / © 2018 IBM Corporation
Data Visualisation
with Python
@MargrietGr

8. matplotlib
DOC ID / Month XX, 2018 / © 2018 IBM Corporation
Data Visualisation
with Python
@MargrietGr

9. matplotlib
@MargrietGr
Data from Barnola et al. Nature, 329, 408-414 (1987)
import pandas as pd
%matplotlib inline
co2_ice = pd.read_csv("data/co2.csv")
co2_ice.plot(x='ice Age', y='CO2 ppmv')

10. matplotlib
Data from Barnola et al. Nature, 329, 408-414 (1987)
import pandas as pd
%matplotlib inline
co2_ice = pd.read_csv("data/co2.csv")
co2_ice.plot(x='ice Age', y='CO2 ppmv')
Data from Barnola
et al. Nature, 329,
408-414 (1987)
@MargrietGr

11. matplotlib
Data from: https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html
@MargrietGr

12. matplotlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
co2_mlo = pd.read_csv("data/co2_weekly_mlo.csv")
co2_mlo = co2_mlo.replace(-999.99, np.nan)
fig, ax = plt.subplots()
ax.plot(co2_mlo['decimal'], co2_mlo['ppm'],'-
',color='#01807D', linewidth=2)
ax.set_title('Observed CO$_2$ at Mauna
Loa',loc='right',fontsize=20)
ax.set_xlabel('year',fontsize=16)
ax.set_ylabel('CO$_2$ (ppmv)',fontsize=16)
ax.set_title('CO$_2$ at Mauna
Loa',loc='right',fontsize=20)
plt.show()
@MargrietGr

13. matplotlib
Seaborn
DOC ID / Month XX, 2018 / © 2018 IBM Corporation
Data Visualisation
with Python
@MargrietGr

14. seaborn
Data from Barnola et al. Nature, 329, 408-414 (1987)
import seaborn as sns
sns.distplot(co2_mlo['ppm'].dropna())
sns.distplot(co2_ice['CO2
ppmv'].dropna())
@MargrietGr

15. seaborn
Data from Barnola et al. Nature, 329, 408-414 (1987)
import seaborn as sns
sns.distplot(co2_mlo['ppm'].dropna())
sns.distplot(co2_ice['CO2
ppmv'].dropna())
@MargrietGr

16. Fun!
But syntax is different
for each package
@MargrietGr

17. matplotlib
Seaborn
Bokeh
Brunel
PdVega
Plotly
…
DOC ID / Month XX, 2018 / © 2018 IBM Corporation
@MargrietGr

18. matplotlib
Seaborn
Bokeh
Brunel
PdVega
Plotly
…
DOC ID / Month XX, 2018 / © 2018 IBM Corporation
@MargrietGr

19. PixieDust
https://pixiedust.github.io
/pixiedust/
@MargrietGr

20. PixieDust
Load data from
anywhere into a Pandas
or Spark DataFrame
@MargrietGr

21. Load data
import pixiedust
pixiedust.sampleData()
@MargrietGr

22. Load data
import pixiedust
pixiedust.sampleData()
@MargrietGr

23. Load data
import pixiedust
df = pixiedust.sampleData(6)
df = pixiedust.sampleData(
"https://openobjectstore.mybluemix.
net/misc/milliondollarhomes.csv")
df = pixiedust.sampleData(6,
forcePandas = True)
@MargrietGr

24. PixieDust
Load data from anywhere
into a Pandas or Spark
DataFrame
Display and filter data
import pixiedust
display(co2_ice)
@MargrietGr

25. PixieDust
Demo
@MargrietGr
http://ibm.biz/forest-notebook

26. PixieDust
Load data from anywhere
into a Pandas or Spark
DataFrame
Display and filter data
Create interactive
dashboard with
PixieApps
@MargrietGr

27. PixieDust
Publish charts and
PixieApps as a web app
using the PixieGateway
Load data from anywhere
into a Pandas or Spark
DataFrame
Display and filter data
Create interactive
dashboard with
PixieApps
@MargrietGr

28. PixieDust
Publish charts and
PixieApps as a web app
using the PixieGateway
Debug code in a notebook
with the PixieDebugger
Load data from anywhere
into a Pandas or Spark
DataFrame
Display and filter data
Create interactive
dashboard with
PixieApps
@MargrietGr

29. PixieDust
Book!
https://www.packtpub.com/
big-data-and-business-
intelligence/thoughtful-data-
science
@MargrietGr

30. import pixiedust
display(df)
@MargrietGr

31. Thank you!
Find me on Twitter
@MargrietGr
@IBMCodeLondon
Slides
http://ibm.biz/slides-
margriet
Notebook
http://ibm.biz/forest-
notebook
Learn more
http://ibm.biz/pycon-uk
Pixiedust
https://pixiedust.github.io/
pixiedust/
Watson Studio
https://dataplatform.cloud.
ibm.com/
@MargrietGr