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Getting Started with SAS® Viya for Python

The SAS Scripting Wrapper for Analytics Transfer (SWAT) package for Python is a Python interface to SAS Cloud Analytic Services (CAS) which is the centerpiece of the SAS Viya framework. With this package, you can load data into memory and apply CAS actions to transform, summarize, model and score the data. CAS can be running on an single machine (SMP) or in a distributed server (MPP). You can still retain the ease-of-use of Python on the client side to further post process CAS result tables.

 

Documentation

SAS Scripting Wrapper for Analytics Transfer API
This native Python API provides direct access to SAS Cloud Analytics Services (CAS), using Python conventions and data structures to execute CAS actions. Data can be accessed and described as Pandas tables.

Getting Started with SAS® Viya for Python
Learn the concepts and capabilities of the Python API for SAS Viya.

 


Examples

Select a sample below and copy and paste into your application.

Get Descriptive Statistics

The example below shows how to use the SUMMARY ACTION to get various descriptive statistics (minimum and maximum values, mean, standardDeviation, etc.) about a data table using SAS Cloud Analytics Services. This example requires that you have in-memory data to analyze.


$ python
In [1]: import swat
In [2]: conn = swat.CAS("myhost.example.com", port, "myCasUserId", "myCasPassword")
In [3]: tbl = conn.read_csv('https://raw.githubusercontent.com/'
   ....:                     'sassoftware/sas-viya-programming/master/data/cars.csv',
   ....:                     casout='cars')
In [4]: out = conn.summary(table=tbl)
In [5]: out
Out[1]: 
[Summary]

 Descriptive Statistics for CARS
 
         Column      Min       Max      N  NMiss          Mean         Sum  \
 0         MSRP  10280.0  192465.0  428.0    0.0  32774.855140  14027638.0   
 1      Invoice   9875.0  173560.0  428.0    0.0  30014.700935  12846292.0   
 2   EngineSize      1.3       8.3  428.0    0.0      3.196729      1368.2   
 3    Cylinders      3.0      12.0  426.0    2.0      5.807512      2474.0   
 4   Horsepower     73.0     500.0  428.0    0.0    215.885514     92399.0   
 5     MPG_City     10.0      60.0  428.0    0.0     20.060748      8586.0   
 6  MPG_Highway     12.0      66.0  428.0    0.0     26.843458     11489.0   
 7       Weight   1850.0    7190.0  428.0    0.0   3577.953271   1531364.0   
 8    Wheelbase     89.0     144.0  428.0    0.0    108.154206     46290.0   
 9       Length    143.0     238.0  428.0    0.0    186.362150     79763.0   
 
             Std      StdErr           Var           USS           CSS  \
 0  19431.716674  939.267478  3.775916e+08  6.209854e+11  1.612316e+11   
 1  17642.117750  852.763949  3.112443e+08  5.184789e+11  1.329013e+11   
 2      1.108595    0.053586  1.228982e+00  4.898540e+03  5.247754e+02   
 3      1.558443    0.075507  2.428743e+00  1.540000e+04  1.032216e+03   
 4     71.836032    3.472326  5.160415e+03  2.215110e+07  2.203497e+06   
 5      5.238218    0.253199  2.743892e+01  1.839580e+05  1.171642e+04   
 6      5.741201    0.277511  3.296139e+01  3.224790e+05  1.407451e+04   
 7    758.983215   36.686838  5.760555e+05  5.725125e+09  2.459757e+08   
 8      8.311813    0.401767  6.908624e+01  5.035958e+06  2.949982e+04   
 9     14.357991    0.694020  2.061519e+02  1.495283e+07  8.802687e+04   
 
           CV      TValue          ProbT  
 0  59.288490   34.894059  4.160412e-127  
 1  58.778256   35.196963  2.684398e-128  
 2  34.679034   59.656105  3.133745e-209  
 3  26.834946   76.913766  1.515569e-251  
 4  33.275059   62.173176  4.185344e-216  
 5  26.111777   79.229235  1.866284e-257  
 6  21.387709   96.729204  1.665621e-292  
 7  21.212776   97.526890  5.812547e-294  
 8   7.685150  269.196577   0.000000e+00  
 9   7.704349  268.525733   0.000000e+00  

+ Elapsed: 0.0229s, user: 0.023s, sys: 0.003s, mem: 1.74mb
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