SAS Viya. Kevin D. Smith

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СКАЧАТЬ style="font-size:15px;">      ....: print('')

      ....:

      int32

      1776

      boolean_false

      False

      list

      ['item1', 'item2', 'item3']

      boolean_true

      True

      int64

      1152921504606846976

      double

      3.14159

      string

      I like snowmen! ☃

      dict

      {'key1': 'value1', 'key3': 3, 'key2': 'value2'}

      Although the object that is returned by an action is always a CASResults object, the contents of that object depend completely on the purpose of that action. It could be as simple as key/value pairs of scalars and as complex as a complex nested structure of dictionaries such as our parameters in the previous section. Actions that perform analytics typically return one or more DataFrames that contain the results.

      Since the results objects are simply Python dictionaries, we assume that you are able to handle operations on them. But we will take a closer look at DataFrames in the next section.

      Using DataFrames

      The DataFrames that are returned by CAS actions are extensions of the DataFrames that are defined by the Pandas package. Largely, both work the same way. The only difference is that the DataFrames returned by CAS contain extra metadata that is found in typical SAS data sets. This metadata includes things such as SAS data format names, the SAS data type, and column and table labels.

      One of the builtins actions that returns a DataFrame is help. This action returns a DataFrame that is filled with the names and descriptions of all the actions that are installed on the server. Each action set gets its own key in the result. Let’s look at some output from help.

      The following code runs the help action, lists the keys in the CASResults object that is returned, verifies that it is a SASDataFrame object using Python’s type function, and displays the contents of the DataFrame (some output is reformatted slightly for readability):

      In [33]: out = conn.help()

      In [34]: list(out.keys())

       Out[34]:

      ['accessControl',

      'builtins',

      'loadStreams',

      'search',

      'session',

      'sessionProp',

      'table',

      'tutorial']

      In [35]: type(out['builtins'])

      Out[35]: swat.dataframe.SASDataFrame

      In [36]: out['builtins']

       Out[36]:

      name description

      0 addNode Adds a machine to the server

      1 removeNode Remove one or more machines from the...

      2 help Shows the parameters for an action o...

      3 listNodes Shows the host names used by the server

      4 loadActionSet Loads an action set for use in this ...

      5 installActionSet Loads an action set in new sessions ...

      6 log Shows and modifies logging levels

      7 queryActionSet Shows whether an action set is loaded

      8 queryName Checks whether a name is an action o...

      9 reflect Shows detailed parameter information...

      10 serverStatus Shows the status of the server

      11 about Shows the status of the server

      12 shutdown Shuts down the server

      13 userInfo Shows the user information for your ...

      14 actionSetInfo Shows the build information from loa...

      15 history Shows the actions that were run in t...

      16 casCommon Provides parameters that are common ...

      17 ping Sends a single request to the server...

      18 echo Prints the supplied parameters to th...

      19 modifyQueue Modifies the action response queue s...

      20 getLicenseInfo Shows the license information for a ...

      21 refreshLicense Refresh SAS license information from...

      22 httpAddress Shows the HTTP address for the serve...

      We can store this DataFrame in another variable to make it a bit easier to work with. Much like Pandas DataFrames, CASResults objects enable you to access keys as attributes (as long as the name of the key doesn’t collide with an existing attribute or method). This means that we can access the builtins key of the out variable in either of the following ways:

      In [37]: blt = out['builtins']

      In [38]: blt = out.builtins

      Which syntax you use depends on personal preference. The dot syntax is a bit cleaner, but the bracketed syntax works regardless of the key value (including white space, or name collisions with existing attributes). Typically, you might use the attribute-style syntax in interactive programming, but the bracketed syntax is better for production code.

      Now that we have a handle on the DataFrame, we can do typical DataFrame operations on it such as sorting and filtering. For example, to sort the builtins actions by the name column, you might do the following.

      In [39]: blt.sort_values('name')

       Out[39]:

      name description

      11 about Shows the status of the server

      14 СКАЧАТЬ