Demo of Brunel on Cars Data

The Data

We read the data into a pandas data frame. In this case we are grabbing some data that represents cars. We read it in and call the brunel use method to ensure the names are usable



In [1]:

    
import pandas as pd
import brunel

cars = pd.read_csv("data/cars.csv")

cars.head(6)









    Out[1]:






  
    
      
      mpg
      cylinders
      engine
      horsepower
      weight
      acceleration
      year
      origin
      name
    
  
  
    
      0
      18.0
      8
      307.0
      130.0
      3504
      12.0
      70
      American
      chevrolet chevelle malibu
    
    
      1
      15.0
      8
      350.0
      165.0
      3693
      11.5
      70
      American
      buick skylark 320
    
    
      2
      18.0
      8
      318.0
      150.0
      3436
      11.0
      70
      American
      plymouth satellite
    
    
      3
      16.0
      8
      304.0
      150.0
      3433
      12.0
      70
      American
      amc rebel sst
    
    
      4
      17.0
      8
      302.0
      140.0
      3449
      10.5
      70
      American
      ford torino
    
    
      5
      15.0
      8
      429.0
      198.0
      4341
      10.0
      70
      American
      ford galaxie 500

Basics

We import the Brunel module and create a couple of simple scatterplots. We use the brunel magic to do so

The basic format of each call to Brunel is simple; whether it is a single line or a set of lines (a cell magic), they are concatenated together, and the result interprested as one command.

This command must start with an ACTION, but may have a set of options at the end specified as ACTION :: OPTIONS.

ACTION is the Brunel action string; OPTIONS are key=value pairs:

data defines the pandas dataframe to use. If not specified, the pandas data that best fits the action command will be used
width and height may be supplied to set the resulting size

For details on the Brunel Action languages, see the Online Docs on Bluemix



In [2]:

    
%brunel data('cars') x(mpg) y(horsepower) color(origin) filter(horsepower)  :: width=800, height=300



In [3]:

    
%brunel bar data('cars') x(origin) y(mpg) mean(mpg) animate(year:6)   :: width=800, height=300



In [4]:

    
%brunel data('cars') edge yrange(origin, year) chord size(#count) color(origin) :: width=500, height=400



In [5]:

    
%brunel data('cars') treemap x(origin, year, cylinders) color(mpg) mean(mpg) size(#count) label(cylinders) tooltip(#all):: width=900, height=600

Using the Dataframe

Since Brunel uses the data frame, we can modify or add to that object to show data in different ways. In the following example we apply a function that takes a name and sees if it matches one of a set of sub-strings. We map this function to the car names to create a new column consisting of the names that match either "Ford" or "Buick", and use that in our Brunel action.

Because the Brunel action is long -- we are adding some CSS styling, we split it into two parts for convenience.



In [6]:

    
def identify(x, search): 
    for y in search: 
        if y.lower() in x.lower(): return y
    return None

cars['Type'] = cars.name.map(lambda x: identify(x, ["Ford", "Buick"]))



In [7]:

    
%%brunel data('cars') x(engine) y(mpg) color(Type)  style('size:50%; fill:#eee') +
     x(engine) y(mpg) color(Type) text style('text {font-size:14; font-weight:bold; fill:darker}')
    :: width=800, height=800



In [ ]:

	mpg	cylinders	engine	horsepower	weight	acceleration	year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	American	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	American	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	American	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	American	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	American	ford torino
5	15.0	8	429.0	198.0	4341	10.0	70	American	ford galaxie 500