01 Workflow Introduction

Revealing your data (nearly) effortlessly,
at every step in your workflow


Workflow from data to decision


If there's no visualization at any of these stages, you're flying blind.

But visualization is often skipped as too hard to construct, particularly for big data.

What if it were simple to visualize anything, anywhere?


Good news/
Bad news


Lots of choices!
Too hard to
try them all,
learn them all, or
get them to work together.


PyViz:



Seamless interoperability
for browser-based
viz tools

Supported by Anaconda, Inc.


Exploring Pandas Dataframes

If your data is in a Pandas dataframe, it's natural to explore it using the .plot() method (based on Matplotlib). Let's look at a dataset of the number of cases of measles and pertussis (per 100,000 people) over time in each state:

In [1]:
import pandas as pd

df = pd.read_csv('../data/diseases.csv.gz')
df.head()
Out[1]:
Year Week State measles pertussis
0 1928 1 Alabama 3.67 NaN
1 1928 2 Alabama 6.25 NaN
2 1928 3 Alabama 7.95 NaN
3 1928 4 Alabama 12.58 NaN
4 1928 5 Alabama 8.03 NaN

Just calling .plot() won't give anything meaningful, because it doesn't know what should be plotted against what:

In [2]:
%matplotlib inline

df.plot();

But with some Pandas operations we can pull out parts of the data that make sense to plot:

In [3]:
import numpy as np

by_year = df[["Year","measles"]].groupby("Year").aggregate(np.sum)
by_year.plot();

Here it is easy to see that the 1963 introduction of a measles vaccine brought the cases down to negligible levels.

Exploring Data with HVPlot and Bokeh

The above plots are just static images, but if you import the hvplot package, you can use the same plotting API to get fully interactive plots with hover, pan, and zoom in a web browser:

In [4]:
import hvplot.pandas

by_year.hvplot()