SWAT is the open-source Python interface to SAS’ cloud-based, fault-tolerant, in-memory analytics server.

Connects to CAS using binary (currently Linux only) or REST interface
Calls CAS analytic actions and returns results in Python objects
Implements a Pandas API that calls CAS actions in the background

How Does it Work?



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import swat



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conn = swat.CAS('cas01', 49786)

Calling CAS Actions



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conn.serverstatus()



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conn.userinfo()



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conn.help();

Loading Data



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tbl2 = conn.read_csv('https://raw.githubusercontent.com/'
                    'sassoftware/sas-viya-programming/master/data/cars.csv', 
                     casout=conn.CASTable('cars'))
tbl2

CASTable

CASTable objects contain a reference to a CAS table as well as filtering and grouping options, and computed columns.



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conn.tableinfo()



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tbl = conn.CASTable('attrition')



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tbl.columninfo()



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tbl2?



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tbl2.fetch()

Exploring Data



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tbl.summary()



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tbl.freq(inputs='Attrition')

Building Analytical Models



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conn.loadactionset('regression')
conn.help(actionset='regression');



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output = tbl.logistic(
    target='Attrition',
    inputs=['Gender', 'MaritalStatus', 'AccountAge'],
    nominals = ['Gender', 'MaritalStatus']
)



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output.keys()



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output



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from swat.render import render_html
render_html(output)

Pandas-style DataFrame API

Many Pandas DataFrame features are available on the CASTable objects.



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import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/'
                 'sassoftware/sas-viya-programming/master/data/cars.csv')
df.describe()



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tbl2.describe()



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tbl2.groupby('Origin').describe()



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tbl[['Gender', 'AccountAge']].head()

Visualization



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from bokeh.plotting import show, figure
from bokeh.charts import Bar
from bokeh.io import output_notebook
output_notebook()



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output1 = tbl.freq(inputs=['Attrition'])

p = Bar(output1['Frequency'], 'FmtVar', 
        values='Frequency',
        color="#1f77b4", 
        agg='mean', 
        title="", 
        xlabel = "Attrition",
        ylabel = 'Frequency',        
        bar_width=0.8,
        plot_width=600, 
        plot_height=400 
)
show(p)



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conn.tableinfo()



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tbl2.groupby(['Origin', 'Type']).describe()



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tbl2[['MPG_CITY', 'MPG_Highway', 'MSRP']].describe()



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tbl2[(tbl2.MSRP > 90000) & (tbl2.Cylinders < 12)].head()



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conn.runcode(code='''
    data cars_temp;
        set cars;
        sqrt_MSRP = sqrt(MSRP);
        MPG_avg = (MPG_city + MPG_highway) / 2;
    run;
''')



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conn.tableinfo()



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conn.loadactionset('fedsql')

conn.fedsql.execdirect(query='''
    select make, model, msrp,
    mpg_highway from cars
        where msrp > 80000 and mpg_highway > 20
''')