Let's assume you are working in postgres and you want want to do row by row operations to do some sort of data manipulation. Your user requires you to keep track of each row's changes and wants to know the number of failures with the updates and the number of successful updates. The output must be in a text file with pretty formatting.
There are many ways to accomplish this task, but let's use Pandas, arcpy.da Update Cursor, and some sql.
#--------------------------------------------------------------------------
def create_tracking_table(sde, tables):
"""
creates a panadas dataframe from a sql statement
Input:
sde - sde connection file
tables - name of the table to get the counts for
Ouput:
Panda Dataframe with column names: Table_Name, Total_Rows and
Processed
"""
desc = arcpy.Describe(sde)
connectionProperties = desc.connectionProperties
username = connectionProperties.user
sql = """SELECT
nspname AS schemaname,relname,reltuples
FROM pg_class C
LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
WHERE
nspname NOT IN ('pg_catalog', 'information_schema') AND
relkind='r' AND
nspname='{schema}' AND
relname in ({tables})
ORDER BY reltuples DESC;""".format(
schema=username,
tables=",".join(["'%s'" % t for t in tables])
)
columns = ['schemaname','Table_Name','Total_Rows']
con = arcpy.ArcSDESQLExecute(sde)
rows = con.execute(sql)
count_df = pd.DataFrame.from_records(rows, columns=columns)
del count_df['schemaname']
count_df['Processed'] = 0 count_df['Errors'] = 0
return count_dfNow we have a function that will return a dataframe object from a SQL statement. It contains 3 fields; Table_Name, Total_Rows, and Processed. Table_name is the name of the table in the database. Total_Rows is the length of the table. Processed is where you are going to modify every a row gets updated successfully. Errors is the numeric column where if an update fails, the value will be added to.
So let's use what we just made:
count_df = create_tracking_table(sde, tables)
for table in tables:
with arcpy.da.UpdateCursor(table, "*") as urows:
for urow in urows:
try:
urow[3] += 1
urows.updateRow(urow)
df.loc[df['Table_Name'] == '%s' % table, 'Processed'] += 1
except:
df.loc[df['Table_Name'] == '%s' % table, 'Errors'] += 1
The pseudo code above shows that whenever an exception is raised, 'Errors' get 1 added to it, and when it successfully updates a row 'Processed' gets updated.
The third part of the task was to output the count table to a text file which can be done easily using the
to_string() method.
with open(
writer.write(count_df.to_string(index=False, col_space=12, justify='left'))
writer.flush()
So there you have it. We have a nice human readable output table in a text file.
Enjoy
