from class_setup import print_with_title
from saspy import SASsession
sas = SASsession()
###############################################################################
# #
# Exercise 10. [Python w/ saspy] Get info about a SAS session #
# #
# Instructions: Uncomment the code immediately below, and then execute #
# #
###############################################################################
# Orginal Version
sas_submit_return_value = sas.submit('PROC PRODUCT_STATUS; RUN;')
sas_submit_log = sas_submit_return_value['LOG']
print_with_title(sas_submit_log, 'SAS log from PROC PRODUCT_STATUS:')
# Verify the output from PROC PRODUCT_STATUS is empty
print('The output from PROC PRODUCT_STATUS appears below.')
print(sas.submit('PROC PRODUCT_STATUS; RUN;')['LST'])
print('The output from PROC PRODUCT_STATUS appears above.')
# Compare the output of PROC PRODUCT_STATUS to PROC SETINIT
print(sas.submit('PROC SETINIT; RUN;')['LOG'])
# Refactor the sas.submit call to match Exercise 9
print(
sas.submit('''
PROC PRODUCT_STATUS;
RUN;
''',
from class_setup import print_with_title import platform ############################################################################### # # # Exercise 0. [Python] Get version number # # # # Instructions: Uncomment the code immediately below, and then execute # # # ############################################################################### print_with_title(platform.sys.version, 'Python version being used:') # Notes: # # 1. The following should be printed: # * Python version information # * Operating System information # # 2. To increase performance, only a small number of modules in Python's # standard library are available by default, so the platform module was # explicitly loaded at the start of this file. # # 3. This example illustrates three ways Python syntax differs from SAS: # * We don't need semicolons at the end of each statement. Unlike SAS, # semicolons are optional in Python, and they are typically only used to
###############################################################################
# #
# Exercise 13. [Python w/ saspy] Imitate the SAS Macro Processor #
# #
# Instructions: Uncomment the code immediately below, and then execute #
# #
###############################################################################
# Original Version
sas_code_fragment = 'proc means data=sashelp.%s; run;'
for dsn in ['fish', 'iris']:
sas_submit_return_value = sas.submit(sas_code_fragment % dsn,
results='TEXT')
print_with_title(
sas_submit_return_value['LST'],
'SAS results from PROC MEANS applies to sashelp.%s:' % dsn)
# Change the SAS procedure used
for dsn in ['fish', 'iris']:
print(f'PROC PRINT applied to sashelp.{dsn}:')
print(
sas.submit(f'proc print data=sashelp.{dsn}(obs=5); run;',
results='TEXT')['LST'])
# Change the datasets used
for dsn in ['cars', 'class']:
print(f'PROC MEAN applied to sashelp.{dsn}:')
print(
sas.submit(f'proc means data=sashelp.{dsn}; run;',
results='TEXT')['LST'])
############################################################################### # # # Exercise 3. [Python] Define a list object # # # # Instructions: Uncomment the code immediately below, and then execute # # # ############################################################################### # Original Version hello_world_list = ['Hello', 'list'] print_with_title(hello_world_list, 'The value of hello_world_list:') print_with_title(type(hello_world_list), 'The type of hello_world_list:') # Print out the initial element of the list print(hello_world_list[0]) # Print out the final element of the list print(hello_world_list[1]) # Create a list of length five, and print its middle elements new_list = list(range(5)) print(new_list[2]) # Notes: #
# Lines 12-14 load modules needed for exercises and should be left as-is
from class_setup import print_with_title
from saspy import SASsession
sas = SASsession()
###############################################################################
# #
# Exercise 11. [Python w/ saspy] Connect directly to a SAS dataset #
# #
# Instructions: Uncomment the code immediately below, and then execute #
# #
###############################################################################
fish_sds = sas.sasdata(table='fish', libref='sashelp')
print_with_title(type(fish_sds), 'The type of fish_sds:')
print_with_title(fish_sds.columnInfo(),
'The Python equivalent of PROC CONTENTS:')
print_with_title(fish_sds.means(), 'The Python equivalent of PROC MEANS:')
# Notes:
# 1. The SASdata object fish_sds (meaning a direct connection to the disk-based
# SAS dataset sashelp.fish, not an in-memory DataFrame) is created, and the
# following are printed:
# * the type of object fish_sds
# * the column-information portion of PROC CONTENTS applied to the SAS
# dataset sashelp.fish
# * summary information about the 7 columns in sashelp.fish
#
# 2. The sas object, which was created at the beginning of this file, is a
# persistent connection to a SAS session, and its sasdata method is used to
# Lines 12-14 load modules needed for exercises and should be left as-is
from class_setup import print_with_title
from saspy import SASsession
sas = SASsession()
###############################################################################
# #
# Exercise 14. [Python w/ saspy] Adding and dropping columns #
# #
# Instructions: Uncomment the code immediately below, and then execute #
# #
###############################################################################
# Original Version
class_df = sas.sasdata2dataframe(table='class', libref='sashelp')
print_with_title(class_df.head(), 'The first 5 rows of class_df:')
class_df['BMI'] = (class_df['Weight'] / class_df['Height']**2) * 703
print_with_title(
class_df.head(),
'The first 5 rows of class_df after a new column has been added:')
class_df.drop(columns=['Height', 'Weight'], inplace=True)
print_with_title(
class_df.head(),
'The first 5 rows of class_df after a two columns have been dropped:')
# Execute the SAS code equivalent
print('The results of the SAS code equivalent:')
print(
sas.submit('''