Introduction to Data Visualization with Altair

Notebook to accompany article on PB Python. http://pbpython.com/altair-intro.html



In [1]:

    
import pandas as pd
from altair import Chart, X, Y, Axis, SortField



In [2]:

    
%matplotlib inline

Read in the sample CSV file containing the MN 2014 capital budget



In [3]:

    
budget = pd.read_csv("https://github.com/chris1610/pbpython/raw/master/data/mn-budget-detail-2014.csv")



In [4]:

    
budget.head()









    Out[4]:






  
    
      
      category
      detail
      amount
    
  
  
    
      0
      UNIVERSITY OF MINNESOTA
      Higher Education Asset Preservation (HEAPR) 1
      42500000
    
    
      1
      UNIVERSITY OF MINNESOTA
      Minneapolis; Tate Laboratory Renovation
      56700000
    
    
      2
      UNIVERSITY OF MINNESOTA
      Crookston; Wellness Center
      10000000
    
    
      3
      UNIVERSITY OF MINNESOTA
      Research Laboratories
      8667000
    
    
      4
      UNIVERSITY OF MINNESOTA
      Duluth; Chemical Sciences and Advanced Materia...
      1500000

Make a basic plot of the top 10 expenditures using the default pandas plotting functions



In [5]:

    
budget_top_10 = budget.sort_values(by='amount',ascending=False)[:10]



In [6]:

    
budget_top_10.plot(kind="bar",x=budget_top_10["detail"],
                   title="MN Capital Budget - 2014",
                   legend=False)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fc3e0cffda0>

Build a similar plot in Altair



In [7]:

    
c = Chart(budget_top_10).mark_bar().encode(
    x='detail',
    y='amount')
c

Convert to a horizontal bar graph



In [8]:

    
c = Chart(budget_top_10).mark_bar().encode(
    y='detail',
    x='amount')
c

Look at the dictionary containing the JSON spec



In [9]:

    
c.to_dict(data=False)









    Out[9]:





{'encoding': {'x': {'field': 'amount', 'type': 'quantitative'},
  'y': {'field': 'detail', 'type': 'nominal'}},
 'mark': 'bar'}

Use X() and Y() - does nothing in this example but is useful for future steps



In [10]:

    
Chart(budget_top_10).mark_bar().encode(
    x=X('detail'),
    y=Y('amount')
)

Add in color codes based on the category. This automatically includes a legend.



In [11]:

    
Chart(budget_top_10).mark_bar().encode(
    x=X('detail'),
    y=Y('amount'),
    color="category"
)

Show what happens if we run on all data, not just the top 10



In [12]:

    
Chart(budget).mark_bar().encode(
    x='detail',
    y='amount',
    color='category')

Filter data to only amounts >= $10M



In [13]:

    
Chart(budget).mark_bar().encode(
    x='detail:N',
    y='amount:Q',
    color='category').transform_data(
      filter='datum.amount >= 10000000',
        )

Swap X and Y in order to see it as a horizontal bar chart



In [14]:

    
Chart(budget).mark_bar().encode(
    y='detail:N',
    x='amount:Q',
    color='category').transform_data(
      filter='datum.amount >= 10000000',
        )

Equivalent approach using X and Y classes



In [15]:

    
Chart(budget).mark_bar().encode(
    x=X('detail:O',
        axis=Axis(title='Project')),
    y=Y('amount:Q',
        axis=Axis(title='2014 Budget')),
    color='category').transform_data(
      filter='datum.amount >= 10000000',
        )

Order the projects based on their total spend



In [16]:

    
Chart(budget).mark_bar().encode(
    x=X('detail:O', sort=SortField(field='amount', order='descending', op='sum'),
        axis=Axis(title='Project')),
    y=Y('amount:Q',
        axis=Axis(title='2014 Budget')),
    color='category').transform_data(
      filter='datum.amount >= 10000000',
        )

Summarize the data at the category level



In [17]:

    
Chart(budget).mark_bar().encode(
    x=X('category', sort=SortField(field='amount', order='descending', op='sum'),
        axis=Axis(title='Category')),
    y=Y('sum(amount)',
        axis=Axis(title='2014 Budget')))



In [18]:

    
c = Chart(budget).mark_bar().encode(
    y=Y('category', sort=SortField(field='amount', order='descending', op='sum'),
        axis=Axis(title='Category')),
    x=X('sum(amount)',
        axis=Axis(title='2014 Budget')))
c



In [19]:

    
c.to_dict(data=False)









    Out[19]:





{'encoding': {'x': {'aggregate': 'sum',
   'axis': {'title': '2014 Budget'},
   'field': 'amount',
   'type': 'quantitative'},
  'y': {'axis': {'title': 'Category'},
   'field': 'category',
   'sort': {'field': 'amount', 'op': 'sum', 'order': 'descending'},
   'type': 'nominal'}},
 'mark': 'bar'}



In [ ]:

	category	detail	amount
0	UNIVERSITY OF MINNESOTA	Higher Education Asset Preservation (HEAPR) 1	42500000
1	UNIVERSITY OF MINNESOTA	Minneapolis; Tate Laboratory Renovation	56700000
2	UNIVERSITY OF MINNESOTA	Crookston; Wellness Center	10000000
3	UNIVERSITY OF MINNESOTA	Research Laboratories	8667000
4	UNIVERSITY OF MINNESOTA	Duluth; Chemical Sciences and Advanced Materia...	1500000