Using Python With SAS Cloud Analytic Services (CAS)

Transcription

1 Using Python With SAS Cloud Analytic Services (CAS) Kevin Smith Software Developer SAS Xiangxiang Meng Sr Product Manager SAS #AnalyticsX C o p y r ig ht 201 6, SAS In sti tute In c. Al l r ig hts r ese rve d.

2 What is it? Open-source Python interface to SAS cloud-based, fault-tolerant, in-memory analytics server.

3 What Does it Do? 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 +

4 In a Nutshell Access to SAS cloud analytics from Python Results as Python objects and Pandas DataFrames Support for familiar Pandas API Issue tracking and collaboration in development through GitHub project + pandas + GitHub

5 SWAT SAS Wrapper for Analytics Transfer

6 How Does it Work? # ipython In[1]: import swat In[2]: conn = swat.cas(host, port, userid, password)

7 Calling CAS Actions In[3]: stat = conn.serverstatus() In[4]: user = conn.userinfo() In[5]: conn.help()

8 Loading Data In[6]: out = conn.loadtable(path=' ', caslib=' ') path= file path, database table, ESP, etc. caslib= configured CAS data connector

9 CASTables CASTable objects contain a reference to a CAS table as well as filtering and grouping options, and computed columns. In[7]: tbl = out.castable # Or, tbl = conn.castable('cas.attrition') In[8]: tbl.columninfo()

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11 Exploring Data Actions that take a CAS table as input can be called directly on the CASTable object. In[9]: tbl.summary() In[10]: tbl.freq(inputs='attrition')

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13 Training Analytical Models In[11]:tbl.logistic( target='attrition', ) inputs=['gender', 'MaritalStatus', 'AccountAge'], nominals = ['Gender', 'MaritalStatus']

14 CAS Python

15 Pandas-style DataFrame API Many Pandas DataFrame features are available on the CASTable objects. In[12]: tbl.describe() In[13]: tbl.groupby('gender').describe() In[14]: tbl[['gender', 'AccountAge']].head()

16 Pandas-style Data Readers Pandas data reader methods can be used on CAS connections as well. In[15]: conn.read_csv( '/path/to/local/file.csv') In[16]: conn.read_sql_query(sqlconn, 'select * from foo')

17 Visualization Bokeh

18 Bokeh In[17]: from bokeh.charts import Bar In[18]: from bokeh.plotting import show, figure In[19]: out = tbl[['attrition']].freq()['frequency'] In[20]: p = Bar(out, 'FmtVar', values='frequency', ) In[21]: show(p)

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20 Slice Dice

21 By Groups By groups work the same way that they do in Pandas. In[22]: tbl.groupby(['origin', 'Make']).describe()

22 Indexing Selecting columns also works like in a Pandas Dataframe. In[23]: tbl[['make', 'Model', 'MSRP']].describe()

23 Indexing Selecting columns also populates the inputs= parameter in CAS actions. In[23]: tbl[['make', 'Model', 'MSRP']].logistic( )

24 Filtering CAS tables can be filtered using boolean indexing just like DataFrames. In[24]: tbl[(tbl.msrp > 90000) & (tbl.cylinders < 12)].head()

25 Python SAS

26 Data Step The runcode CAS action executes Data step code. In[24]: conn.runcode(code=''' data cars_temp; set cars; sqrt_msrp = sqrt(msrp); MPG_avg = (MPG_city + MPG_highway) / 2; run; ''')

27 SQL The fedsql.execdirect CAS action executes SQL code. In[24]: conn.fedsql.execdirect(query=''' select make, model, msrp, mpg_highway from cars where msrp > and mpg_highway > 20 ''')

28 ODS-style Rendering

29 SWAT Demo

30 #AnalyticsX C o p y r ig ht 201 6, SAS In sti tute In c. Al l r ig hts r ese rve d.