DataQualityFrameworkGovernance 1.0.0.0

Data Quality Framework Governance is a structured approach to assessing, monitoring, and improving the quality of data.

Data Quality Framework Governance (DQFG)

Data Quality Framework Governance is a structured approach to assessing, monitoring, and improving the quality of data. An effective Data Quality Framework considers these dimensions and integrates them into a structured approach to ensure that data serves its intended purpose, supports informed decision-making, and maintains the trust of users and stakeholders.

Data Quality is an ongoing process that requires continuous monitoring, assessment, and improvement to adapt to changing data requirements and evolving business needs.

Example: To call functions from the library.

from DataQualityFrameworkGovernance import Uniqueness as uq
print(uq.duplicate_rows(dataframe))

Data Quality Framework (DataWorkflow & DataPipeline)

DataWorkflow.DataPipeline

processing_framework

User configures DataframeDictionary and DataFunctionConfig in JSON file, based on the JSON file, data pipeline tasks will be performed in processing framework.

import pandas as pd
from DataQualityFrameworkGovernance.DataWorkflow import DataPipeline as dp

json_config_file = 'https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/dq_pipeline_config.json'
#output_csv = 'full path of system location to save the output / result - [OPTIONAL, if the result to be saved in a CSV file]'

print(dp.processing_framework(json_config_file))

The output_csv parameter is optional in 'processing_framework' function, and if specified, the result will be saved exclusively in CSV file format. Please provide the full path, including the desired CSV file name, for saving the output.

Refer DataWorkflow, Pre-configured Json

Result

DateTime	DQ#	Dataset_name	DQ_Dimension	DQ_Rule/Function_name	Error_in_JSON	Error log	DQ_Rule/Function_description	DQ_Valid_count	DQ_Invalid_count	DQ_Total_count	DQ_Valid%	DQ_Invalid%	DQ_Flag_Inclusion	Data_enabled	Function_enabled	Parameter_enabled	Source
2023-12-23 11:44:13.324231	DQFGCP02	ecomm_data	Completeness	DataQualityFrameworkGovernance.Completeness.missing_values_in_column	No	No error in JSON config	"Parameter: df: 'ecomm_data', columnname: 'address2', calculate: 'Yes'"	7	43	50	14.000000000000002	86.0	Y	True	True	True	https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv
2023-12-23 11:44:13.339754	DQFGCP01	ecomm_data	Completeness	DataQualityFrameworkGovernance.Completeness.missing_values_in_dataset	No	No error in JSON config	"Parameter: df: 'ecomm_data', calculate: 'Yes'"	770	80	50	90.58823529411765	9.411764705882353	Y	True	True	True	https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv
2023-12-23 11:44:13.361530	DQFGAC01	ecomm_data	Accuracy	DataQualityFrameworkGovernance.Accuracy.accuracy_tolerance_numeric	No	No error in JSON config	"Parameter: df: 'ecomm_data', base_column: 'actual_price', lookup_column: 'discounted_price', tolerance_percentage: '0', calculate: 'Yes'"	0	50	50	0.0	100.0	Y	True	True	True	https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv
2023-12-23 11:44:13.379107	DQFGAC02	ecomm_data	Accuracy	DataQualityFrameworkGovernance.Accuracy.accurate_number_range	No	No error in JSON config	"Parameter: df: 'ecomm_data', range_column_name: 'actual_price', lower_bound: '1', upper_bound: '1000', calculate: 'Yes'"	50	0	50	100.0	0.0	Y	True	True	True	https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv
2023-12-23 11:44:13.395435	DQFGAC03	ecomm_data	Accuracy	DataQualityFrameworkGovernance.Accuracy.accurate_datetime_range	No	No error in JSON config	"Parameter: df: 'ecomm_data', range_column_name: 'purchase_datetime', from_date: '2023-05-01', to_date: '2023-05-28', date_format: '%Y-%m-%d', calculate: 'Yes'"	22	28	50	44.0	56.00000000000001	Y	True	True	True	https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv

Library structure

Accuracy

accuracy_tolerance_numeric

Calculating data quality accuracy of a set of values (base values) by comparing them to a known correct value (lookup value) by setting a user-defined tolerance percentage, applicable for numeric values.

from  DataQualityFrameworkGovernance  import  Accuracy as ac
print(ac.accuracy_tolerance_numeric(dataframe, 'base_column', 'lookup_column', tolerance_percentage))

accurate_number_range

Number range ensures that data values are accurate and conform to expected values or constraints. It is applicable to a variety of contexts, including exam scores, weather conditions, pricing, stock prices, age, income, speed limits for vehicles, water levels, and numerous other scenarios.

from  DataQualityFrameworkGovernance  import  Accuracy as ac
print(ac.accurate_number_range(dataframe, 'range_column_name', lower_bound, upper_bound))

Example:
print(ac.accurate_number_range(df, 'Age', 4, 12))
(Output will extract the age between 4(lower bound) and 12 (upper bound) from column 'Age' in the dataset 'df')

accurate_datetime_range

The datetime range filter guarantees the accuracy and adherence of data values to predetermined criteria or constraints. It is applicable to a variety of contexts, including capturing outliers in date of birth, age and many more.

from  DataQualityFrameworkGovernance  import  Accuracy as ac
print(ac.accurate_datetime_range(Dataframe, 'range_column_name', 'from_date', 'to_date', 'date_format'))

Example:
print(ac.accurate_datetime_range(df, 'Date', '2023-01-15', '2023-03-01', '%Y-%m-%d'))

Important: Specify date format in '%Y-%m-%d %H:%M:%S.%f' (It can be specified in any format aligned to source date format).

Completeness

missing_values_in_column

Summary of missing values in each column.

from  DataQualityFrameworkGovernance  import  Completeness as cp
print(cp.missing_values_in_column(dataframe))

missing_values_in_dataset

Summary of missing values in a dataset.

from  DataQualityFrameworkGovernance  import  Completeness as cp
print(cp.missing_values_in_dataset(dataframe))

Consistency

start_end_date_consistency

If data in two columns is consistent, check if the "Start Date" and "End Date" column are in the correct chronological order.

from  DataQualityFrameworkGovernance  import  Consistency as ct
#print(ct.start_end_date_consistency(dataframe, 'start_date_column_name', 'end_date_column_name', date_format))

df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
print(ct.start_end_date_consistency(df, 'purchase_datetime', 'refund_date','%Y-%m-%d %H:%M:%S'))

Important: Specify date format in '%Y-%m-%d %H:%M:%S.%f' (It can be specified in any format aligned to source date format).

Uniqueness

duplicate_rows

Identify and display duplicate rows in a dataset.

from  DataQualityFrameworkGovernance  import  Uniqueness as uq
print(uq.duplicate_rows(dataframe))

unique_column_values

Display unique column values in a dataset.

from  DataQualityFrameworkGovernance  import  Uniqueness as uq
print(uq.unique_column_values(dataframe, 'column_name'))

Validity

validate_age

Validate age based on the criteria in a dataset.

from  DataQualityFrameworkGovernance  import  Validity as vl
print(vl.validate_age(dataframe, 'age_column', min_age, max_age))

vaild_email_pattern

Validating accuracy of email addresses in a dataset by verifying that they follow a valid email format.

from  DataQualityFrameworkGovernance  import  Validity as vl
print(vl.valid_email_pattern(dataframe,'email_column_name'))

isMetadataMember

If all values in a given array list are present in a specific column of a dataset then it provides a status message indicating whether all names are found or not. Array values must be within square brackets.

#Examples
#array list = ["Tom", "Jerry", "Donald"] - Text
#array list = [10, 20, 30] - Numeric
#array list = [True, False] - Boolean
#array list = [0, 1] - Flag

from  DataQualityFrameworkGovernance  import  Validity as vl
df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
meta_member = ["Yes", "N"]

print(vl.isMetadataMember(df, 'replaced', meta_member,"No"))
#print(vl.isMetadataMember(dataframe, 'column_name_to_look', [array_list]))
#Parameter 'No' is optional to enable and disable calculation

is_column_numeric

Examines each value in a column and appends a new column to the existing column, indicating whether the column is numeric.

from  DataQualityFrameworkGovernance  import  Validity as vl
df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
print(vl.is_column_numeric(df, 'actual_price'))

#print(vl.is_column_numeric(dataframe, 'column_name'))

contains_number_in_column

Examines each value in a column and appends a new column to the existing column, indicating whether the values contains numeric.

from  DataQualityFrameworkGovernance  import  Validity as vl
df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
print(vl.contains_number_in_column(df, 'product_name'))

#print(vl.contains_number_in_column(dataframe, 'column_name'))

is_number_in_dataset

Examines each value in a dataset and appends a new column for each existing column, indicating whether the values are numeric.

from  DataQualityFrameworkGovernance  import  Validity as vl
print(vl.is_number_in_dataset(dataframe))

#Example for specific column selection
is_number_in_dataset(dataframe[['column1','column7']])

textonly_in_column

Examines each value in a column and appends a new column to the existing column, indicating whether the values are text. Result would be false, if text or string contains number.

from  DataQualityFrameworkGovernance  import  Validity as vl
df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
print(vl.textonly_in_column(df,'product_description'))

#print(vl.textonly_in_column(dataframe, 'column_name'))

is_text_in_dataset

Examines each value in a dataset and appends a new column for each existing column, indicating whether the values are text. Result would be false, if text or string contains number.

from  DataQualityFrameworkGovernance  import  Validity as vl
print(vl.is_text_in_dataset(dataframe))

#Example for specific column selection
is_text_in_dataset(dataframe[['column1','column7']])

is_date_in_column

Examines each value in a column and appends a new column to the existing column, indicating whether the values are in date time, in a speciifed format.

from  DataQualityFrameworkGovernance  import  Validity as vl
print(vl.is_date_in_column(dataframe,'column_name', date_format))

Important: Specify date format in '%Y-%m-%d %H:%M:%S.%f' (It can be specified in any format aligned to source date format).

is_date_in_dataset

Examines each value in a dataset and appends a new column for each existing column, indicating whether the values are in date time, in a speciifed format.

from  DataQualityFrameworkGovernance  import  Validity as vl
df = pd.read_csv('https://raw.githubusercontent.com/RajithPrabakaran/DataQualityFrameworkGovernance/main/Files/ecommerce_dataset.csv')
print(vl.is_date_in_dataset(df,'%Y-%m-%d %H:%M:%S'))

#print(vl.is_date_in_dataset(df[['purchase_datetime','product_name']],'%Y-%m-%d %H:%M:%S'))
#print(vl.is_date_in_dataset(dataframe, date_format))

#Example for specific column selection
is_date_in_dataset(dataframe[['column1','column7']], date_format='%Y-%m-%d')

Important: Specify date format in '%Y-%m-%d %H:%M:%S.%f' (It can be specified in any format aligned to source date format).

Datastats

count_rows

Count the number of rows in a DataFrame.

from  DataQualityFrameworkGovernance  import  Datastats as ds
print(ds.count_rows(dataframe))

count_columns

Count the number of columns in a DataFrame.

from  DataQualityFrameworkGovernance  import  Datastats as ds
print(ds.count_columns(dataframe))

count_dataset

Count the number of rows & columns in a DataFrame.

from  DataQualityFrameworkGovernance  import  Datastats as ds
print(ds.count_dataset(dataframe))

limit_max_length

Limits the maximum length of a string to specific length. Example, when applied to the input string 'ABCDEFGH', the function returns 'ABCDE', effectively truncating the original string to the first 5 characters.

from  DataQualityFrameworkGovernance  import  Datastats as ds
print(ds.limit_max_length(dataframe, column_name, start_length, length))

#Example: 'ABCDEFGH' input string returns 'ABCDE'
print(limit_max_length(df,'column_name',0,5))

Data Interoperability

data_migration_reconciliation

Data migration reconciliation is a crucial step in ensuring the accuracy and integrity of data transfer between a source and target system. The process involves comparison of the source and target data to identify any disparities. If the columns in both datasets differ, the process returns an ouput to align the source and target dataset.

Output of column name mismatch

Column	MatchStatus	TableLocation
Department	Unmatched	Source
Departmentt	Unmatched	Target
EmployeeID	Matched	NotApplicable

After structural alignment is confirmed, a comprehensive check is performed by comparing the content of each column. Any inconsistencies between the source and target data are flagged as mismatches. This includes the identification of specific 'column name(s)' where discrepancies occur, 'row number or position' and 'mismatched records' in both the source and target datasets. This comprehensive reporting ensures that discrepancies can be easily located and addressed, promoting data accuracy and the successful completion of the migration process.

from  DataQualityFrameworkGovernance  import  Interoperability as io
print(io.data_migration_reconciliation(source_dataframe, target_dataframe))

#Example of saving source and target dataframe from csv file

import pandas as pd
source_dataframe = pd.read_csv('source_data.csv')
target_dataframe = pd.read_csv('target_data.csv')

Result

Column	Row no. / Position	Source Data	Target Data
Column name	2	33	3
Column name	289	Donald Trump	Donald Duck

data_integration_reconciliation

Data integration reconciliation involves combining data from different sources into a unified view. This function compares two datasets, source_dataset and target_dataset, based on a unique identifier, ID. It checks for disparities in each column, cell by cell, between the two datasets. For each mismatch, it identifies the specific column and provides a status of "Matched" or "Mismatched." If the columns in both datasets differ, the process returns an ouput to align the source and target dataset.

Example output of column name mismatch

Column	MatchStatus	TableLocation
Department	Unmatched	Source
Departmentt	Unmatched	Target
EmployeeID	Matched	NotApplicable

After structural alignment is confirmed, a comprehensive check is performed by comparing the content of each column. Any inconsistencies between the source and target data are flagged as mismatches.

Parameters:

source_dataset: The source dataset, a DataFrame containing the data to be compared. target_dataset: The target dataset, a DataFrame containing the data to be compared against the source dataset ID: A unique identifier column present in both datasets, used to match rows between the two datasets.

Return Value:

status: A string indicating the overall comparison status, either "Matched" or "Mismatched." mismatched_columns: A list of columns that have mismatches between the two datasets.

import pandas as pd
from  DataQualityFrameworkGovernance  import  Interoperability as io

source_dataset = pd.DataFrame({
	'Ordinal': [54, 55, 56, 57],
	'Name': ['Theresa May','Boris Johnson', 'Liz Truss', 'Rishi Sunak'],
	'Monarch': ['Elizabeth II', 'Elizabeth II', 'Elizabeth II & Charles III', 'Charles III']
	})

target_dataset = pd.DataFrame({
	'Ordinal': [55, 56, 57],
	'Name': ['Boris Johnson', 'Liz Truss', 'Rishi Sunak'],
	'Monarch': ['Elizabeth II', 'Elizabeth II', 'Charles III']
	})

comparison_results = io.data_integration_reconciliation(source_dataset, target_dataset, 'Ordinal')
print(comparison_results)

Result

Ordinal	Status	Mismatched_Columns	MergeStatus	Name_source	Name_target	Monarch_source	Monarch_target
54	Mismatch	Name, Monarch	left_only	Theresa May	NaN	Elizabeth II	NaN
56	Mismatch	Monarch	both	Liz Truss	Liz Truss	Elizabeth II & Charles III	Elizabeth II
55	Match	None	both	Boris Johnson	Boris Johnson	Elizabeth II	Elizabeth II
57	Match	None	both	Rishi Sunak	Rishi Sunak	Charles III	Charles III

data_consolidation

Data consolidation is a process of combining information from multiple datasets to create a unified dataset. This function with three parameters – dataset1, dataset2, and a parameter to determine consolidation direction (0 for rows ,1 for columns), users can choose between consolidating data by rows or columns.

Compile by Rows (0):

When choosing compile=0, the function will stack the datasets vertically, effectively appending the rows of dataset2 beneath the rows of dataset1.

Compile by Columns (1):

Alternatively, selecting compile=1 will concatenate the datasets side by side, merging columns from dataset2 to the right of those from dataset1.

from  DataQualityFrameworkGovernance  import  Interoperability as io

CompileByColumns = io.data_consolidation(df1, df2,1)
CompileByRows = io.data_consolidation(df1, df2,0)

Supporting python libraries:

• Pandas, re, requests

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