def cleanUpAddress(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
# First replace the escape character with comma + space
dataFlow = dataFlow.str_replace(operationFlag, r'\0d0a', ', ')
# Now capture any empty addresses
dataFlow = dataFlow.replace(operationFlag, r', , , ', None)
print('{0}: cleaned up addresses in column {1}'.format(
dataName, operationFlag))
# Will now try to split out the address into component columns...
# builder = dataFlow.builders.split_column_by_example(source_column=operationFlag)
# builder.add_example(example=('552 Malvern Avenue, St. George, Bristol', \
# ['552 Malvern Avenue', 'St. George', 'Bristol']))
# builder.add_example(example=('224A Tomswood St, Hainault, Ilford, Cornwall', \
# ['224A Tomswood St', 'Hainault', 'Ilford', 'Cornwall']))
# dataFlow = builder.to_dataflow()
# dataFlow = dataFlow.split_column_by_delimiters(operationFlag, ', ', True)
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def removeDuplicates(dataName, previousStageNumber, thisStageNumber,
qualityFlag, operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
columnsToKeep = operationFlag
numberOfRowsBefore = dataFlow.row_count
dataFlow = dataFlow.distinct(
dprep.ColumnSelector(columnsToKeep, True, True, invert=False))
print(
'{0}: removed duplicates from column {1} rows before {2} rows afer {3}'
.format(dataName, operationFlag, numberOfRowsBefore,
dataFlow.row_count))
else:
print('{0}: no duplicate processing required'.format(dataName))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def automaticallyDetectColumnTypes(dataName, previousStageNumber,
thisStageNumber, qualityFlag, operatorToUse,
operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
# Now perform the operation on the dataFlow : ie remove the number of rows specified from the top
# Perform the operation
if operationFlag == 'Yes':
# Detect and apply column types
builder = dataFlow.builders.set_column_types()
builder.learn()
print('{0}: candidates detected {1}'.format(
dataName, builder.conversion_candidates))
builder.ambiguous_date_conversions_keep_month_day()
dataFlow = builder.to_dataflow()
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def parseNulls(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
# Get a list of the columns and count them...
dataFlowColumns = list(dataFlow.get_profile().columns.keys())
# Replace any occurences null, using custom, across all columns...
dataFlow = dataFlow.replace_na(dataFlowColumns,
custom_na_list=operationFlag)
print(
'{0}: parsed nulls including custom string {1} from {2} columns'
.format(dataName, operationFlag, len(dataFlowColumns)))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def removeRowsFromTop(dataName, previousStageNumber, thisStageNumber,
qualityFlag, operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
# Now perform the operation on the dataFlow : ie remove the number of rows specified from the top
numberOfRowsToRemove = int(operationFlag)
dataFlow = dataFlow.skip(numberOfRowsToRemove)
print('{0}: removed first {1} row(s)'.format(dataName,
numberOfRowsToRemove))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
# Now return all of the components badk to the main loop...
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def renameColumns(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
# Do the operation on columns to rename them...
print('{0}: renamed {1} columns'.format(dataName, operationFlag))
else:
print('{0}: no operation to perform'.format(dataName))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def mapLookups(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
transforms = load_transformation_configuration('./Config/' +
operationFlag)
if len(transforms) > 1:
lookups = get_lookups_from_transforms(transforms)
for key in lookups:
lookupDictionary = lookups[key]
replacements = []
dataFlow = dataFlow.set_column_types(
{key: dprep.FieldType.STRING})
for lookup in lookupDictionary:
replacements.append(
ReplacementsValue(lookup,
lookupDictionary[lookup]))
destination_column = get_destination_column_name(
key, transforms)
dataFlow = dataFlow.map_column(key, destination_column,
replacements)
print(dataName + ': Transformed lookups for column - ' +
key + '. Added new column ' + destination_column)
else:
print('{0}: no look-up processing required'.format(dataName))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def createUPMDataflow(dataName, previousStageNumber, thisStageNumber,
qualityFlag, operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
if operationFlag != '':
mappingConfig = dprep.read_csv(
'./Config/' + operationFlag).to_pandas_dataframe()
targetDataFlow = dataFlow
columnsToKeep = ''
for sourceTable in mappingConfig[mappingConfig.SourceTable ==
dataName]['SourceTable'].unique():
for sourceColumn, targetColumn in mappingConfig[
mappingConfig.SourceTable == sourceTable][[
'SourceColumn', 'TargetColumn'
]].values:
if columnsToKeep is '':
columnsToKeep = targetColumn
else:
columnsToKeep = columnsToKeep + '|' + targetColumn
targetDataFlow = targetDataFlow.rename_columns(
{sourceColumn: targetColumn})
targetDataFlow = targetDataFlow.drop_columns(
dprep.ColumnSelector(columnsToKeep, True, True, invert=True))
newPackageName = next(
iter(mappingConfig[mappingConfig.SourceTable == dataName]
['TargetTable'].unique()))
createNewPackageDirectory(newPackageName)
saveDataFlowPackage(targetDataFlow, newPackageName,
thisStageNumber, 'A')
else:
print('{0}: no duplicate processing required'.format(dataName))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
if operationFlag == 'Yes':
print('{0}: loading data from file path {1}'.format(
dataName, fullFilePath))
newDataFlow = dprep.read_csv(fullFilePath)
dataProfile = newDataFlow.get_profile()
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
columnInventoryAll = columnInventoryAll.append(columnInventory)
print('{0}: generated column inventory'.format(dataName))
dataFlowInventory = getDataFlowStats(newDataFlow, dataProfile,
dataName, thisStageNumber,
operatorToUse, operationFlag)
dataFlowInventoryAll = dataFlowInventoryAll.append(dataFlowInventory)
print('{0}: generated data flow inventory'.format(dataName))
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(newDataFlow, dataName,
thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
else:
print('{0}: no package file created.'.format(dataName))
# Once we have processed all dataflows, we save the inventories away
saveColumnInventory(columnInventoryAll, thisStageNumber)
saveDataFlowInventory(dataFlowInventoryAll, thisStageNumber)
def splitTableBasedOnSingleColumn(dataName, previousStageNumber,
thisStageNumber, qualityFlag, operatorToUse,
operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
dataProfile = dataFlow.get_profile()
# Set up empty intermediate dataframes that we will use to build up inventories at both dataFlow and column level
dataFlowInventoryIntermediate = pd.DataFrame()
columnInventoryIntermediate = pd.DataFrame()
if operationFlag != '':
# First, grab the unique set of values in the column
valuesInColumn = dataProfile.columns[operationFlag].value_counts
# Now filter the original data flow based on each unique value in turn and fork a new data flow!
for valueToSplitOn in valuesInColumn:
newDataFlow = dataFlow.filter(
dataFlow[operationFlag] == valueToSplitOn.value)
# Create a new name for this data flow based on concatenation of source dataflow, column name and value used for filter
newDataName = dataName + '_' + operationFlag + '_' + valueToSplitOn.value
newDataProfile = newDataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(newDataProfile, newDataName,
thisStageNumber,
operatorToUse, operationFlag)
dataFlowInventory = getDataFlowStats(
newDataFlow, newDataProfile, newDataName, thisStageNumber,
operatorToUse, operationFlag)
# Capture the column inventory for the new dataflow
columnInventoryIntermediate = columnInventoryIntermediate.append(
columnInventory)
# Capture the data flow inventory for the new data flow
dataFlowInventoryIntermediate = dataFlowInventoryIntermediate.append(
dataFlowInventory)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(
newDataFlow, newDataName, thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(
newDataName, targetPackagePath))
else:
print('{0}: no operation required'.format(dataName))
# Generate column and data flow inventories for the source dataflow
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
columnInventoryIntermediate = columnInventoryIntermediate.append(
columnInventory)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventoryIntermediate = dataFlowInventoryIntermediate.append(
dataFlowInventory)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
# Now return all of the components badk to the main loop...
return dataFlow, columnInventoryIntermediate, dataFlowInventoryIntermediate
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def quarantineRows(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
# Now perform the operation on the dataFlow : ie remove the number of rows specified from the top
# First count the number of columns found
dataFlowColumns = list(dataFlow.get_profile().columns.keys())
numberOfColumnsFound = len(dataFlowColumns)
# Now convert the operationFlag to an integer
headerCount = int(operationFlag)
# If we have more columns that expected, we quarantine rows which have values in the extra columns
if numberOfColumnsFound > headerCount:
# NOTE - this logic assumes that all unwanted columns are on the far right, this could be improved!
# Fork a new data flow with rows that have data in the un-expected columns
print(
'{0}: we have found {1} columns, expected {2} so will now quarantine any rows with data in them '
.format(dataName, numberOfColumnsFound, headerCount))
quarantinedDataFlow = dataFlow.drop_nulls(
dataFlowColumns[headerCount:])
# There is a chance we have an extra column but no rows to quarantine, so check this first
if quarantinedDataFlow.row_count is None:
quarantinedRowCount = 0
print('{0}: no rows to quarantine'.format(dataName))
else:
quarantinedRowCount = quarantinedDataFlow.row_count
# Finally save the data flow so it can be used later
fullPackagePath = saveDataFlowPackage(quarantinedDataFlow,
dataName,
thisStageNumber, 'B')
print('{0}: quarantined {1} rows of data to {2}'.format(
dataName, quarantinedRowCount, fullPackagePath))
# Now filter out the quarantined rows from the main data set
# NOTE : can't figure out a better way of doign this for now - see note below...
for columnToCheck in dataFlowColumns[headerCount:]:
# NOTE - don't know why commented line of code below doesn't work!
# dataFlow = dataFlow.filter(dataFlow[columnToCheck] != '')
dataFlow = dataFlow.assert_value(columnToCheck,
value != '',
error_code='ShouldBeNone')
dataFlow = dataFlow.filter(col(columnToCheck).is_error())
print('{0}: filtered column {1}, row count now {2}'.format(
dataName, columnToCheck, dataFlow.row_count))
# Finally drop the extra columns
dataFlow = dataFlow.drop_columns(dataFlowColumns[headerCount:])
print('{0}: dropped {1} unwanted columns'.format(
dataName, len(dataFlowColumns[headerCount:])))
else:
print(
'{0}: we have found {1} columns, expected {2} so not going to do anything'
.format(dataName, numberOfColumnsFound, headerCount))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved package to {1}'.format(dataName, targetPackagePath))
# Now return all of the components badk to the main loop...
return dataFlow, columnInventory, dataFlowInventory
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
def joinTables(dataName, previousStageNumber, thisStageNumber, qualityFlag,
operatorToUse, operationFlag):
dataFlow, fullPackagePath = openDataFlowPackage(dataName,
previousStageNumber,
qualityFlag)
if dataFlow:
print('{0}: loaded package from path {1}'.format(
dataName, fullPackagePath))
# Set up empty intermediate dataframes that we will use to build up inventories at both dataFlow and column level
dataFlowInventoryIntermediate = pd.DataFrame()
columnInventoryIntermediate = pd.DataFrame()
if operationFlag != '':
# Load config file
joinConfig = dprep.read_csv('./Config/' +
operationFlag).to_pandas_dataframe()
# For each config in the file...
for index, row in joinConfig.iterrows():
leftDataName = row['LeftDataName']
leftDataFlowJoinColumn = row['LeftDataFlowJoinColumn']
rightDataName = row['RightDataName']
rightDataFlowJoinColumn = row['RightDataFlowJoinColumn']
joinType = row['JoinType']
print(
'{0}: ready to join {1} {2} -> {3} {4} using jointype {5}'.
format(dataName, leftDataName, leftDataFlowJoinColumn,
rightDataName, rightDataFlowJoinColumn, joinType))
# Load right hand data flow
rightDataFlow, fullPackagePath = openDataFlowPackage(
rightDataName, previousStageNumber, qualityFlag)
print('{0}: loaded package from path {1}'.format(
rightDataName, fullPackagePath))
# We always perform the inner "MATCH" stype join
join_builder = dataFlow.builders.join(
right_dataflow=rightDataFlow,
left_column_prefix=dataName + '_',
right_column_prefix=rightDataName + '_')
join_builder.detect_column_info()
join_builder.join_key_pairs = [(leftDataFlowJoinColumn,
rightDataFlowJoinColumn)]
# Setting up join type:
# NONE = 0
# MATCH = 2
# UNMATCHLEFT = 4
# UNMATCHRIGHT = 8
join_builder.join_type = 2
innerDataFlow = join_builder.to_dataflow()
print('{0} created inner dataflow : Columns : {1}, Rows : {2}'.
format(dataName,
len(innerDataFlow.get_profile().columns),
innerDataFlow.row_count))
if joinType == "LEFT":
# Use the "UNMATCHLEFT" setting to grab the rows that haven't been joined from the left data flow
join_builder.join_type = 4
leftUnmatchedDataFlow = join_builder.to_dataflow()
print(
'{0} created left unmatched dataflow : Columns : {1}, Rows : {2}'
.format(
dataName,
len(leftUnmatchedDataFlow.get_profile().columns),
leftUnmatchedDataFlow.row_count))
# Now append this dataflow to the original inner join dataflow, to create a "left outer join"
newDataFlow = innerDataFlow.append_rows(
[leftUnmatchedDataFlow])
else:
newDataFlow = innerDataFlow
# Create a new name for this data flow based on concatenation of left dataflow and right
newDataName = dataName + '_' + rightDataName
# Output key stats
print('{0} left table : {0}, Columns : {1}, Rows : {2}'.format(
leftDataName, len(dataFlow.get_profile().columns),
dataFlow.row_count))
print(
'{0} right table : {0}, Columns : {1}, Rows : {2}'.format(
rightDataName,
len(rightDataFlow.get_profile().columns),
rightDataFlow.row_count))
newDataProfile = newDataFlow.get_profile()
print(
'{0} joined table : {0}, Columns : {1}, Rows : {2}'.format(
newDataName, len(newDataProfile.columns),
newDataFlow.row_count))
# Now generate column and data flow inventories
columnInventory = getColumnStats(newDataProfile, newDataName,
thisStageNumber,
operatorToUse, operationFlag)
dataFlowInventory = getDataFlowStats(
newDataFlow, newDataProfile, newDataName, thisStageNumber,
operatorToUse, operationFlag)
# Capture the column inventory for the new dataflow
columnInventoryIntermediate = columnInventoryIntermediate.append(
columnInventory)
# Capture the data flow inventory for the new data flow
dataFlowInventoryIntermediate = dataFlowInventoryIntermediate.append(
dataFlowInventory)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(
newDataFlow, newDataName, thisStageNumber, 'A')
print('{0}: saved package to {1}'.format(
newDataName, targetPackagePath))
else:
print('{0}: no joining of tables required'.format(dataName))
dataProfile = dataFlow.get_profile()
# Now generate column and data flow inventories
columnInventory = getColumnStats(dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
columnInventoryIntermediate = columnInventoryIntermediate.append(
columnInventory)
dataFlowInventory = getDataFlowStats(dataFlow, dataProfile, dataName,
thisStageNumber, operatorToUse,
operationFlag)
dataFlowInventoryIntermediate = dataFlowInventoryIntermediate.append(
dataFlowInventory)
# Finally save the data flow so it can be passed onto the next stage of the process...
targetPackagePath = saveDataFlowPackage(dataFlow, dataName,
thisStageNumber, qualityFlag)
print('{0}: saved source package to {1}'.format(
dataName, targetPackagePath))
return dataFlow, columnInventoryIntermediate, dataFlowInventoryIntermediate
else:
print('{0}: no package file found at location {1}'.format(
dataName, fullPackagePath))
return None, None, None
