A Python Package for Data Profiling and Data Quality Auditing
Project description
Acknowledgment
This package is a part of the Master of Science Program in Applied Statistics (Business Analytics and Intelligence: BA&I), National Institute of Development Administration(NIDA), Thailand
Version 2.8.12
Sakdas is a data profiling and auditing package based on statistical methods.
Its primary goals are to provide self-service data profiling to understand the data via statistical and visualization and data auditing to audit the data via a predefined condition.
Features
- Analyze the data profile based on statistical methods and visualization
- Audit the data by using a predefined condition.
- Provide audited dataset(CSV) with auditing result tag or without tag, duplicated dataset.
- Generate data profiling and auditing reports.
Installation
pip install pandas
pip install matplotlib
pip install sakdas
Upgrade
pip install sakdas --upgrade
Profiling Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>')
Auditing Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } sample_supermarket_sales = sd( df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config)
Sakda Methods
| Methods | Return |
|---|---|
| dataset = sd(Path to CSV file) | dataset object and profiling report |
| dataset.audit(audit_config) | data profiling and auditing report |
Sakda Attributes
Attrubutes: sakda.dataset_name
Return type: String
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.dataset_name)
Output
sample_supermarket_sales
Attrubute: sakda.df
Return type: pandas.core.frame.DataFrame
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.df)
Output
Example data in DataFrame
Attrubute: sakda.duplicated_record
Return type: pandas.core.frame.DataFrame
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.duplicated_record)
Output
Example duplicated data in DataFrame
Attrubute: sakda.schema
Return type: list
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.schema)
Output
===Schema===
Invoice_ID: object
Branch: object
City: object
Customer_type: object
Gender: object
Product_line: object
Unit_price: float64
Quantity: int64
Tax: float64
Total: float64
Date: object
Time: object
Payment: object
cogs: float64
gross_margin_percentage: float64
gross_income: float64
Rating: float64
Attrubute: sakda.schema_json
Return type: list
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.schema_json)
Output
[{'columnName': 'Invoice_ID', 'dataType': 'object'}, {'columnName': 'Branch', 'dataType': 'object'}, {'columnName': 'City', 'dataType': 'object'}, {'columnName': 'Customer_type', 'dataType': 'object'}, {'columnName': 'Gender', 'dataType': 'object'}, {'columnName': 'Product_line', 'dataType': 'object'}, {'columnName': 'Unit_price', 'dataType': 'float64'}, {'columnName': 'Quantity', 'dataType': 'int64'}, {'columnName': 'Tax', 'dataType': 'float64'}, {'columnName': 'Total', 'dataType': 'float64'}, {'columnName': 'Date', 'dataType': 'object'}, {'columnName': 'Time', 'dataType': 'object'}, {'columnName': 'Payment', 'dataType': 'object'}, {'columnName': 'cogs', 'dataType': 'float64'}, {'columnName': 'gross_margin_percentage', 'dataType': 'float64'}, {'columnName': 'gross_income', 'dataType': 'float64'}, {'columnName': 'Rating', 'dataType': 'float64'}]
Attrubute: sakda.profile
Return type: dict
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.profile)
Output
{'profile_engine': 'sakdas beta version 0.2.15', 'profile_id': 'b6e810ab-701f-489d-ac58-98515b021159', 'file_name': 'sample_supermarket_sales', 'profile_datetime': '23/09/2020 10:52:47', 'total_record': 1000, 'total_record_after_deduplication': 1000, 'duplicated_data': '0', 'primary_key': 'Invoice_ID', 'total_column': 17, 'completed_record': '1000', 'completed_record_ratio': '1.0', 'blank_column': 0, 'missing_condition': 'null', 'missing_data': '0', 'missing_data_ratio': '0.0', 'schema': '===Schema===\nInvoice_ID: object\nBranch: object\nCity: object\nCustomer_type: object\nGender: object\nProduct_line: object\nUnit_price: float64\nQuantity: int64\nTax: float64\nTotal: float64\nDate: object\nTime: object\nPayment: object\ncogs: float64\ngross_margin_percentage: float64\ngross_income: float64\nRating: float64', 'column_profile': {'Invoice_ID': {'dataType': 'object', 'is_primary_key': 'True', 'distinct_value': '1000', 'ratio_distinct_value': '1.0', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '11', 'data_lenght_max': '11', 'top_5_data_value': [{'data_value': '101-17-6199', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '641-62-7288', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '633-91-1052', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '634-97-8956', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '635-28-5728', 'count_data_value': '1', 'value_ratio': '0.001'}], 'top_5_data_pattern': [{'pattern': 'XXX-XX-XXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Branch': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '1', 'data_lenght_max': '1', 'top_5_data_value': [{'data_value': 'A', 'count_data_value': '340', 'value_ratio': '0.34'}, {'data_value': 'B', 'count_data_value': '332', 'value_ratio': '0.332'}, {'data_value': 'C', 'count_data_value': '328', 'value_ratio': '0.328'}], 'top_5_data_pattern': [{'pattern': 'X', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'City': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '6', 'data_lenght_max': '9', 'top_5_data_value': [{'data_value': 'Yangon', 'count_data_value': '340', 'value_ratio': '0.34'}, {'data_value': 'Mandalay', 'count_data_value': '332', 'value_ratio': '0.332'}, {'data_value': 'Naypyitaw', 'count_data_value': '328', 'value_ratio': '0.328'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '340', 'pattern_ratio': '0.34'}, {'pattern': 'XXXXXXXX', 'count_pattern': '332', 'pattern_ratio': '0.332'}, {'pattern': 'XXXXXXXXX', 'count_pattern': '328', 'pattern_ratio': '0.328'}]}, 'Customer_type': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '2', 'ratio_distinct_value': '0.002', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '6', 'data_lenght_max': '6', 'top_5_data_value': [{'data_value': 'Member', 'count_data_value': '501', 'value_ratio': '0.501'}, {'data_value': 'Normal', 'count_data_value': '499', 'value_ratio': '0.499'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Gender': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '2', 'ratio_distinct_value': '0.002', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '6', 'top_5_data_value': [{'data_value': 'Female', 'count_data_value': '501', 'value_ratio': '0.501'}, {'data_value': 'Male', 'count_data_value': '499', 'value_ratio': '0.499'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '501', 'pattern_ratio': '0.501'}, {'pattern': 'XXXX', 'count_pattern': '499', 'pattern_ratio': '0.499'}]}, 'Product_line': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '6', 'ratio_distinct_value': '0.006', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '17', 'data_lenght_max': '22', 'top_5_data_value': [{'data_value': 'Fashion accessories', 'count_data_value': '178', 'value_ratio': '0.178'}, {'data_value': 'Food and beverages', 'count_data_value': '174', 'value_ratio': '0.174'}, {'data_value': 'Electronic accessories', 'count_data_value': '170', 'value_ratio': '0.17'}, {'data_value': 'Sports and travel', 'count_data_value': '166', 'value_ratio': '0.166'}, {'data_value': 'Home and lifestyle', 'count_data_value': '160', 'value_ratio': '0.16'}], 'top_5_data_pattern': [{'pattern': 'XXXX XXX XXXXXXXXX', 'count_pattern': '334', 'pattern_ratio': '0.334'}, {'pattern': 'XXXXXX XXX XXXXXX', 'count_pattern': '318', 'pattern_ratio': '0.318'}, {'pattern': 'XXXXXXX XXXXXXXXXXX', 'count_pattern': '178', 'pattern_ratio': '0.178'}, {'pattern': 'XXXXXXXXXX XXXXXXXXXXX', 'count_pattern': '170', 'pattern_ratio': '0.17'}]}, 'Unit_price': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '943', 'ratio_distinct_value': '0.943', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '5', 'min_value': '10.08', 'max_value': '99.96', 'mean': '55.67', 'median': '55', 'mode': '83.77', 'variance': '701.97', 'std': '26.49', 'first_quartile': '33', 'third_quartile': '78', 'iqr': '45', 'minimum': '10.5', 'maximum': '100.5', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '5', 'top_5_data_value': [{'data_value': '83.77', 'count_data_value': '3.0', 'value_ratio': '0.003'}, {'data_value': '99.96', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '24.74', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '45.38', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '45.58', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XX', 'count_pattern': '903', 'pattern_ratio': '0.903'}, {'pattern': 'XX.X', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Quantity': {'dataType': 'int64', 'is_primary_key': 'False', 'distinct_value': '10', 'ratio_distinct_value': '0.01', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '1', 'data_lenght_max': '2', 'min_value': '1', 'max_value': '10', 'mean': '5.51', 'median': '5', 'mode': '10', 'variance': '8.55', 'std': '2.92', 'first_quartile': '3', 'third_quartile': '8', 'iqr': '5', 'minimum': '0.5', 'maximum': '10.5', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '10', 'count_data_value': '119', 'value_ratio': '0.119'}, {'data_value': '1', 'count_data_value': '112', 'value_ratio': '0.112'}, {'data_value': '4', 'count_data_value': '109', 'value_ratio': '0.109'}, {'data_value': '5', 'count_data_value': '102', 'value_ratio': '0.102'}, {'data_value': '7', 'count_data_value': '102', 'value_ratio': '0.102'}], 'top_5_data_pattern': [{'pattern': 'X', 'count_pattern': '881', 'pattern_ratio': '0.881'}, {'pattern': 'XX', 'count_pattern': '119', 'pattern_ratio': '0.119'}]}, 'Tax': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '18', 'min_value': '0.51', 'max_value': '49.65', 'mean': '15.38', 'median': '12', 'mode': '4.15', 'variance': '137.1', 'std': '11.71', 'first_quartile': '6', 'third_quartile': '22', 'iqr': '16', 'minimum': '-2.0', 'maximum': '30.0', 'negative_outliner_datapoint': '137', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '39.48', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '13.187999999999999', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '22.428', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.4639999999999995', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.154', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XXX', 'count_pattern': '259', 'pattern_ratio': '0.259'}, {'pattern': 'X.XXX', 'count_pattern': '195', 'pattern_ratio': '0.195'}, {'pattern': 'XX.XXXX', 'count_pattern': '127', 'pattern_ratio': '0.127'}, {'pattern': 'X.XXXX', 'count_pattern': '107', 'pattern_ratio': '0.107'}, {'pattern': 'XX.XX', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Total': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '18', 'min_value': '10.68', 'max_value': '1042.65', 'mean': '322.97', 'median': '254', 'mode': '87.23', 'variance': '60459.6', 'std': '245.89', 'first_quartile': '124', 'third_quartile': '471', 'iqr': '347', 'minimum': '-49.5', 'maximum': '644.5', 'negative_outliner_datapoint': '131', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '829.08', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '276.948', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '470.98800000000006', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '93.744', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '87.234', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XXX.XXX', 'count_pattern': '358', 'pattern_ratio': '0.358'}, {'pattern': 'XXX.XXXX', 'count_pattern': '166', 'pattern_ratio': '0.166'}, {'pattern': 'XXX.XX', 'count_pattern': '120', 'pattern_ratio': '0.12'}, {'pattern': 'XXX.XXXXXXXXXXXXXX', 'count_pattern': '106', 'pattern_ratio': '0.106'}, {'pattern': 'XX.XXX', 'count_pattern': '91', 'pattern_ratio': '0.091'}]}, 'Date': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '89', 'ratio_distinct_value': '0.089', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '8', 'data_lenght_max': '9', 'top_5_data_value': [{'data_value': '2/7/2019', 'count_data_value': '20', 'value_ratio': '0.02'}, {'data_value': '2/15/2019', 'count_data_value': '19', 'value_ratio': '0.019'}, {'data_value': '1/8/2019', 'count_data_value': '18', 'value_ratio': '0.018'}, {'data_value': '3/2/2019', 'count_data_value': '18', 'value_ratio': '0.018'}, {'data_value': '3/14/2019', 'count_data_value': '18', 'value_ratio': '0.018'}], 'top_5_data_pattern': [{'pattern': 'X/XX/XXXX', 'count_pattern': '677', 'pattern_ratio': '0.677'}, {'pattern': 'X/X/XXXX', 'count_pattern': '323', 'pattern_ratio': '0.323'}]}, 'Time': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '506', 'ratio_distinct_value': '0.506', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '5', 'data_lenght_max': '5', 'top_5_data_value': [{'data_value': '19:48', 'count_data_value': '7', 'value_ratio': '0.007'}, {'data_value': '14:42', 'count_data_value': '7', 'value_ratio': '0.007'}, {'data_value': '17:38', 'count_data_value': '6', 'value_ratio': '0.006'}, {'data_value': '17:36', 'count_data_value': '5', 'value_ratio': '0.005'}, {'data_value': '19:44', 'count_data_value': '5', 'value_ratio': '0.005'}], 'top_5_data_pattern': [{'pattern': 'XX:XX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Payment': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '11', 'top_5_data_value': [{'data_value': 'Ewallet', 'count_data_value': '345', 'value_ratio': '0.345'}, {'data_value': 'Cash', 'count_data_value': '344', 'value_ratio': '0.344'}, {'data_value': 'Credit card', 'count_data_value': '311', 'value_ratio': '0.311'}], 'top_5_data_pattern': [{'pattern': 'XXXXXXX', 'count_pattern': '345', 'pattern_ratio': '0.345'}, {'pattern': 'XXXX', 'count_pattern': '344', 'pattern_ratio': '0.344'}, {'pattern': 'XXXXXX XXXX', 'count_pattern': '311', 'pattern_ratio': '0.311'}]}, 'cogs': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '6', 'min_value': '10.17', 'max_value': '993.0', 'mean': '307.59', 'median': '242', 'mode': '83.08', 'variance': '54838.64', 'std': '234.18', 'first_quartile': '118', 'third_quartile': '449', 'iqr': '331', 'minimum': '-47.5', 'maximum': '614.5', 'negative_outliner_datapoint': '131', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '789.6', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '263.76', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '448.56', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '89.28', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '83.08', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XXX.XX', 'count_pattern': '534', 'pattern_ratio': '0.534'}, {'pattern': 'XXX.X', 'count_pattern': '242', 'pattern_ratio': '0.242'}, {'pattern': 'XX.XX', 'count_pattern': '185', 'pattern_ratio': '0.185'}, {'pattern': 'XX.X', 'count_pattern': '39', 'pattern_ratio': '0.039'}]}, 'gross_margin_percentage': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '1', 'ratio_distinct_value': '0.001', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '18', 'data_lenght_max': '18', 'min_value': '4.76', 'max_value': '4.76', 'mean': '4.76', 'median': '5', 'mode': '4.76', 'variance': '0.0', 'std': '0.0', 'first_quartile': '5', 'third_quartile': '5', 'iqr': '0', 'minimum': '5.0', 'maximum': '5.0', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '1000', 'top_5_data_value': [{'data_value': '4.7619047619999995', 'count_data_value': '1000.0', 'value_ratio': '1.0'}], 'top_5_data_pattern': [{'pattern': 'X.XXXXXXXXXXXXXXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'gross_income': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '18', 'min_value': '0.51', 'max_value': '49.65', 'mean': '15.38', 'median': '12', 'mode': '4.15', 'variance': '137.1', 'std': '11.71', 'first_quartile': '6', 'third_quartile': '22', 'iqr': '16', 'minimum': '-2.0', 'maximum': '30.0', 'negative_outliner_datapoint': '137', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '39.48', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '13.187999999999999', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '22.428', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.4639999999999995', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.154', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XXX', 'count_pattern': '259', 'pattern_ratio': '0.259'}, {'pattern': 'X.XXX', 'count_pattern': '195', 'pattern_ratio': '0.195'}, {'pattern': 'XX.XXXX', 'count_pattern': '127', 'pattern_ratio': '0.127'}, {'pattern': 'X.XXXX', 'count_pattern': '107', 'pattern_ratio': '0.107'}, {'pattern': 'XX.XX', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Rating': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '61', 'ratio_distinct_value': '0.061', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '4', 'min_value': '4.0', 'max_value': '10.0', 'mean': '6.97', 'median': '7', 'mode': '6.0', 'variance': '2.95', 'std': '1.72', 'first_quartile': '6', 'third_quartile': '8', 'iqr': '2', 'minimum': '5.0', 'maximum': '9.0', 'negative_outliner_datapoint': '151', 'positive_outliner_datapoint': '153', 'top_5_data_value': [{'data_value': '6.0', 'count_data_value': '26.0', 'value_ratio': '0.026'}, {'data_value': '6.6', 'count_data_value': '24.0', 'value_ratio': '0.024'}, {'data_value': '9.5', 'count_data_value': '22.0', 'value_ratio': '0.022'}, {'data_value': '4.2', 'count_data_value': '22.0', 'value_ratio': '0.022'}, {'data_value': '5.1', 'count_data_value': '21.0', 'value_ratio': '0.021'}], 'top_5_data_pattern': [{'pattern': 'X.X', 'count_pattern': '995', 'pattern_ratio': '0.995'}, {'pattern': 'XX.X', 'count_pattern': '5', 'pattern_ratio': '0.005'}]}}}
Attrubute: sakda.profile_json
Return type: string
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.profile_json)
Output
{
"profile_engine": "sakdas beta version 0.2.15",
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"variance": "2.95",
"std": "1.72",
"first_quartile": "6",
"third_quartile": "8",
"iqr": "2",
"minimum": "5.0",
"maximum": "9.0",
"negative_outliner_datapoint": "151",
"positive_outliner_datapoint": "153",
"top_5_data_value": [
{
"data_value": "6.0",
"count_data_value": "26.0",
"value_ratio": "0.026"
},
{
"data_value": "6.6",
"count_data_value": "24.0",
"value_ratio": "0.024"
},
{
"data_value": "9.5",
"count_data_value": "22.0",
"value_ratio": "0.022"
},
{
"data_value": "4.2",
"count_data_value": "22.0",
"value_ratio": "0.022"
},
{
"data_value": "5.1",
"count_data_value": "21.0",
"value_ratio": "0.021"
}
],
"top_5_data_pattern": [
{
"pattern": "X.X",
"count_pattern": "995",
"pattern_ratio": "0.995"
},
{
"pattern": "XX.X",
"count_pattern": "5",
"pattern_ratio": "0.005"
}
]
}
}
}
Attrubute: sakda.column_profile
Return type: dict
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.column_profile)
Output
{'Invoice_ID': {'dataType': 'object', 'is_primary_key': 'True', 'distinct_value': '1000', 'ratio_distinct_value': '1.0', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '11', 'data_lenght_max': '11', 'top_5_data_value': [{'data_value': '101-17-6199', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '641-62-7288', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '633-91-1052', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '634-97-8956', 'count_data_value': '1', 'value_ratio': '0.001'}, {'data_value': '635-28-5728', 'count_data_value': '1', 'value_ratio': '0.001'}], 'top_5_data_pattern': [{'pattern': 'XXX-XX-XXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Branch': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '1', 'data_lenght_max': '1', 'top_5_data_value': [{'data_value': 'A', 'count_data_value': '340', 'value_ratio': '0.34'}, {'data_value': 'B', 'count_data_value': '332', 'value_ratio': '0.332'}, {'data_value': 'C', 'count_data_value': '328', 'value_ratio': '0.328'}], 'top_5_data_pattern': [{'pattern': 'X', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'City': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '6', 'data_lenght_max': '9', 'top_5_data_value': [{'data_value': 'Yangon', 'count_data_value': '340', 'value_ratio': '0.34'}, {'data_value': 'Mandalay', 'count_data_value': '332', 'value_ratio': '0.332'}, {'data_value': 'Naypyitaw', 'count_data_value': '328', 'value_ratio': '0.328'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '340', 'pattern_ratio': '0.34'}, {'pattern': 'XXXXXXXX', 'count_pattern': '332', 'pattern_ratio': '0.332'}, {'pattern': 'XXXXXXXXX', 'count_pattern': '328', 'pattern_ratio': '0.328'}]}, 'Customer_type': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '2', 'ratio_distinct_value': '0.002', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '6', 'data_lenght_max': '6', 'top_5_data_value': [{'data_value': 'Member', 'count_data_value': '501', 'value_ratio': '0.501'}, {'data_value': 'Normal', 'count_data_value': '499', 'value_ratio': '0.499'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Gender': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '2', 'ratio_distinct_value': '0.002', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '6', 'top_5_data_value': [{'data_value': 'Female', 'count_data_value': '501', 'value_ratio': '0.501'}, {'data_value': 'Male', 'count_data_value': '499', 'value_ratio': '0.499'}], 'top_5_data_pattern': [{'pattern': 'XXXXXX', 'count_pattern': '501', 'pattern_ratio': '0.501'}, {'pattern': 'XXXX', 'count_pattern': '499', 'pattern_ratio': '0.499'}]}, 'Product_line': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '6', 'ratio_distinct_value': '0.006', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '17', 'data_lenght_max': '22', 'top_5_data_value': [{'data_value': 'Fashion accessories', 'count_data_value': '178', 'value_ratio': '0.178'}, {'data_value': 'Food and beverages', 'count_data_value': '174', 'value_ratio': '0.174'}, {'data_value': 'Electronic accessories', 'count_data_value': '170', 'value_ratio': '0.17'}, {'data_value': 'Sports and travel', 'count_data_value': '166', 'value_ratio': '0.166'}, {'data_value': 'Home and lifestyle', 'count_data_value': '160', 'value_ratio': '0.16'}], 'top_5_data_pattern': [{'pattern': 'XXXX XXX XXXXXXXXX', 'count_pattern': '334', 'pattern_ratio': '0.334'}, {'pattern': 'XXXXXX XXX XXXXXX', 'count_pattern': '318', 'pattern_ratio': '0.318'}, {'pattern': 'XXXXXXX XXXXXXXXXXX', 'count_pattern': '178', 'pattern_ratio': '0.178'}, {'pattern': 'XXXXXXXXXX XXXXXXXXXXX', 'count_pattern': '170', 'pattern_ratio': '0.17'}]}, 'Unit_price': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '943', 'ratio_distinct_value': '0.943', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '5', 'min_value': '10.08', 'max_value': '99.96', 'mean': '55.67', 'median': '55', 'mode': '83.77', 'variance': '701.97', 'std': '26.49', 'first_quartile': '33', 'third_quartile': '78', 'iqr': '45', 'minimum': '10.5', 'maximum': '100.5', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '5', 'top_5_data_value': [{'data_value': '83.77', 'count_data_value': '3.0', 'value_ratio': '0.003'}, {'data_value': '99.96', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '24.74', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '45.38', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '45.58', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XX', 'count_pattern': '903', 'pattern_ratio': '0.903'}, {'pattern': 'XX.X', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Quantity': {'dataType': 'int64', 'is_primary_key': 'False', 'distinct_value': '10', 'ratio_distinct_value': '0.01', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '1', 'data_lenght_max': '2', 'min_value': '1', 'max_value': '10', 'mean': '5.51', 'median': '5', 'mode': '10', 'variance': '8.55', 'std': '2.92', 'first_quartile': '3', 'third_quartile': '8', 'iqr': '5', 'minimum': '0.5', 'maximum': '10.5', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '10', 'count_data_value': '119', 'value_ratio': '0.119'}, {'data_value': '1', 'count_data_value': '112', 'value_ratio': '0.112'}, {'data_value': '4', 'count_data_value': '109', 'value_ratio': '0.109'}, {'data_value': '5', 'count_data_value': '102', 'value_ratio': '0.102'}, {'data_value': '7', 'count_data_value': '102', 'value_ratio': '0.102'}], 'top_5_data_pattern': [{'pattern': 'X', 'count_pattern': '881', 'pattern_ratio': '0.881'}, {'pattern': 'XX', 'count_pattern': '119', 'pattern_ratio': '0.119'}]}, 'Tax': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '18', 'min_value': '0.51', 'max_value': '49.65', 'mean': '15.38', 'median': '12', 'mode': '4.15', 'variance': '137.1', 'std': '11.71', 'first_quartile': '6', 'third_quartile': '22', 'iqr': '16', 'minimum': '-2.0', 'maximum': '30.0', 'negative_outliner_datapoint': '137', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '39.48', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '13.187999999999999', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '22.428', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.4639999999999995', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.154', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XXX', 'count_pattern': '259', 'pattern_ratio': '0.259'}, {'pattern': 'X.XXX', 'count_pattern': '195', 'pattern_ratio': '0.195'}, {'pattern': 'XX.XXXX', 'count_pattern': '127', 'pattern_ratio': '0.127'}, {'pattern': 'X.XXXX', 'count_pattern': '107', 'pattern_ratio': '0.107'}, {'pattern': 'XX.XX', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Total': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '18', 'min_value': '10.68', 'max_value': '1042.65', 'mean': '322.97', 'median': '254', 'mode': '87.23', 'variance': '60459.6', 'std': '245.89', 'first_quartile': '124', 'third_quartile': '471', 'iqr': '347', 'minimum': '-49.5', 'maximum': '644.5', 'negative_outliner_datapoint': '131', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '829.08', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '276.948', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '470.98800000000006', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '93.744', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '87.234', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XXX.XXX', 'count_pattern': '358', 'pattern_ratio': '0.358'}, {'pattern': 'XXX.XXXX', 'count_pattern': '166', 'pattern_ratio': '0.166'}, {'pattern': 'XXX.XX', 'count_pattern': '120', 'pattern_ratio': '0.12'}, {'pattern': 'XXX.XXXXXXXXXXXXXX', 'count_pattern': '106', 'pattern_ratio': '0.106'}, {'pattern': 'XX.XXX', 'count_pattern': '91', 'pattern_ratio': '0.091'}]}, 'Date': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '89', 'ratio_distinct_value': '0.089', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '8', 'data_lenght_max': '9', 'top_5_data_value': [{'data_value': '2/7/2019', 'count_data_value': '20', 'value_ratio': '0.02'}, {'data_value': '2/15/2019', 'count_data_value': '19', 'value_ratio': '0.019'}, {'data_value': '1/8/2019', 'count_data_value': '18', 'value_ratio': '0.018'}, {'data_value': '3/2/2019', 'count_data_value': '18', 'value_ratio': '0.018'}, {'data_value': '3/14/2019', 'count_data_value': '18', 'value_ratio': '0.018'}], 'top_5_data_pattern': [{'pattern': 'X/XX/XXXX', 'count_pattern': '677', 'pattern_ratio': '0.677'}, {'pattern': 'X/X/XXXX', 'count_pattern': '323', 'pattern_ratio': '0.323'}]}, 'Time': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '506', 'ratio_distinct_value': '0.506', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '5', 'data_lenght_max': '5', 'top_5_data_value': [{'data_value': '19:48', 'count_data_value': '7', 'value_ratio': '0.007'}, {'data_value': '14:42', 'count_data_value': '7', 'value_ratio': '0.007'}, {'data_value': '17:38', 'count_data_value': '6', 'value_ratio': '0.006'}, {'data_value': '17:36', 'count_data_value': '5', 'value_ratio': '0.005'}, {'data_value': '19:44', 'count_data_value': '5', 'value_ratio': '0.005'}], 'top_5_data_pattern': [{'pattern': 'XX:XX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'Payment': {'dataType': 'object', 'is_primary_key': 'False', 'distinct_value': '3', 'ratio_distinct_value': '0.003', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '11', 'top_5_data_value': [{'data_value': 'Ewallet', 'count_data_value': '345', 'value_ratio': '0.345'}, {'data_value': 'Cash', 'count_data_value': '344', 'value_ratio': '0.344'}, {'data_value': 'Credit card', 'count_data_value': '311', 'value_ratio': '0.311'}], 'top_5_data_pattern': [{'pattern': 'XXXXXXX', 'count_pattern': '345', 'pattern_ratio': '0.345'}, {'pattern': 'XXXX', 'count_pattern': '344', 'pattern_ratio': '0.344'}, {'pattern': 'XXXXXX XXXX', 'count_pattern': '311', 'pattern_ratio': '0.311'}]}, 'cogs': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '4', 'data_lenght_max': '6', 'min_value': '10.17', 'max_value': '993.0', 'mean': '307.59', 'median': '242', 'mode': '83.08', 'variance': '54838.64', 'std': '234.18', 'first_quartile': '118', 'third_quartile': '449', 'iqr': '331', 'minimum': '-47.5', 'maximum': '614.5', 'negative_outliner_datapoint': '131', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '789.6', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '263.76', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '448.56', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '89.28', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '83.08', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XXX.XX', 'count_pattern': '534', 'pattern_ratio': '0.534'}, {'pattern': 'XXX.X', 'count_pattern': '242', 'pattern_ratio': '0.242'}, {'pattern': 'XX.XX', 'count_pattern': '185', 'pattern_ratio': '0.185'}, {'pattern': 'XX.X', 'count_pattern': '39', 'pattern_ratio': '0.039'}]}, 'gross_margin_percentage': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '1', 'ratio_distinct_value': '0.001', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '18', 'data_lenght_max': '18', 'min_value': '4.76', 'max_value': '4.76', 'mean': '4.76', 'median': '5', 'mode': '4.76', 'variance': '0.0', 'std': '0.0', 'first_quartile': '5', 'third_quartile': '5', 'iqr': '0', 'minimum': '5.0', 'maximum': '5.0', 'negative_outliner_datapoint': '0', 'positive_outliner_datapoint': '1000', 'top_5_data_value': [{'data_value': '4.7619047619999995', 'count_data_value': '1000.0', 'value_ratio': '1.0'}], 'top_5_data_pattern': [{'pattern': 'X.XXXXXXXXXXXXXXXX', 'count_pattern': '1000', 'pattern_ratio': '1.0'}]}, 'gross_income': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '990', 'ratio_distinct_value': '0.99', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '18', 'min_value': '0.51', 'max_value': '49.65', 'mean': '15.38', 'median': '12', 'mode': '4.15', 'variance': '137.1', 'std': '11.71', 'first_quartile': '6', 'third_quartile': '22', 'iqr': '16', 'minimum': '-2.0', 'maximum': '30.0', 'negative_outliner_datapoint': '137', 'positive_outliner_datapoint': '0', 'top_5_data_value': [{'data_value': '39.48', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '13.187999999999999', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '22.428', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.4639999999999995', 'count_data_value': '2.0', 'value_ratio': '0.002'}, {'data_value': '4.154', 'count_data_value': '2.0', 'value_ratio': '0.002'}], 'top_5_data_pattern': [{'pattern': 'XX.XXX', 'count_pattern': '259', 'pattern_ratio': '0.259'}, {'pattern': 'X.XXX', 'count_pattern': '195', 'pattern_ratio': '0.195'}, {'pattern': 'XX.XXXX', 'count_pattern': '127', 'pattern_ratio': '0.127'}, {'pattern': 'X.XXXX', 'count_pattern': '107', 'pattern_ratio': '0.107'}, {'pattern': 'XX.XX', 'count_pattern': '97', 'pattern_ratio': '0.097'}]}, 'Rating': {'dataType': 'float64', 'is_primary_key': 'False', 'distinct_value': '61', 'ratio_distinct_value': '0.061', 'missing_value': '0', 'ratio_missing_value': '0.0', 'data_lenght_min': '3', 'data_lenght_max': '4', 'min_value': '4.0', 'max_value': '10.0', 'mean': '6.97', 'median': '7', 'mode': '6.0', 'variance': '2.95', 'std': '1.72', 'first_quartile': '6', 'third_quartile': '8', 'iqr': '2', 'minimum': '5.0', 'maximum': '9.0', 'negative_outliner_datapoint': '151', 'positive_outliner_datapoint': '153', 'top_5_data_value': [{'data_value': '6.0', 'count_data_value': '26.0', 'value_ratio': '0.026'}, {'data_value': '6.6', 'count_data_value': '24.0', 'value_ratio': '0.024'}, {'data_value': '9.5', 'count_data_value': '22.0', 'value_ratio': '0.022'}, {'data_value': '4.2', 'count_data_value': '22.0', 'value_ratio': '0.022'}, {'data_value': '5.1', 'count_data_value': '21.0', 'value_ratio': '0.021'}], 'top_5_data_pattern': [{'pattern': 'X.X', 'count_pattern': '995', 'pattern_ratio': '0.995'}, {'pattern': 'XX.X', 'count_pattern': '5', 'pattern_ratio': '0.005'}]}}
Attrubute: dataset.column_profile_json
Return type: string
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>') print(get_profile.column_profile_json)
Output
{
"Invoice_ID": {
"dataType": "object",
"is_primary_key": "True",
"distinct_value": "1000",
"ratio_distinct_value": "1.0",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "11",
"data_lenght_max": "11",
"top_5_data_value": [
{
"data_value": "101-17-6199",
"count_data_value": "1",
"value_ratio": "0.001"
},
{
"data_value": "641-62-7288",
"count_data_value": "1",
"value_ratio": "0.001"
},
{
"data_value": "633-91-1052",
"count_data_value": "1",
"value_ratio": "0.001"
},
{
"data_value": "634-97-8956",
"count_data_value": "1",
"value_ratio": "0.001"
},
{
"data_value": "635-28-5728",
"count_data_value": "1",
"value_ratio": "0.001"
}
],
"top_5_data_pattern": [
{
"pattern": "XXX-XX-XXXX",
"count_pattern": "1000",
"pattern_ratio": "1.0"
}
]
},
"Branch": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "3",
"ratio_distinct_value": "0.003",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "1",
"data_lenght_max": "1",
"top_5_data_value": [
{
"data_value": "A",
"count_data_value": "340",
"value_ratio": "0.34"
},
{
"data_value": "B",
"count_data_value": "332",
"value_ratio": "0.332"
},
{
"data_value": "C",
"count_data_value": "328",
"value_ratio": "0.328"
}
],
"top_5_data_pattern": [
{
"pattern": "X",
"count_pattern": "1000",
"pattern_ratio": "1.0"
}
]
},
"City": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "3",
"ratio_distinct_value": "0.003",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "6",
"data_lenght_max": "9",
"top_5_data_value": [
{
"data_value": "Yangon",
"count_data_value": "340",
"value_ratio": "0.34"
},
{
"data_value": "Mandalay",
"count_data_value": "332",
"value_ratio": "0.332"
},
{
"data_value": "Naypyitaw",
"count_data_value": "328",
"value_ratio": "0.328"
}
],
"top_5_data_pattern": [
{
"pattern": "XXXXXX",
"count_pattern": "340",
"pattern_ratio": "0.34"
},
{
"pattern": "XXXXXXXX",
"count_pattern": "332",
"pattern_ratio": "0.332"
},
{
"pattern": "XXXXXXXXX",
"count_pattern": "328",
"pattern_ratio": "0.328"
}
]
},
"Customer_type": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "2",
"ratio_distinct_value": "0.002",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "6",
"data_lenght_max": "6",
"top_5_data_value": [
{
"data_value": "Member",
"count_data_value": "501",
"value_ratio": "0.501"
},
{
"data_value": "Normal",
"count_data_value": "499",
"value_ratio": "0.499"
}
],
"top_5_data_pattern": [
{
"pattern": "XXXXXX",
"count_pattern": "1000",
"pattern_ratio": "1.0"
}
]
},
"Gender": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "2",
"ratio_distinct_value": "0.002",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "4",
"data_lenght_max": "6",
"top_5_data_value": [
{
"data_value": "Female",
"count_data_value": "501",
"value_ratio": "0.501"
},
{
"data_value": "Male",
"count_data_value": "499",
"value_ratio": "0.499"
}
],
"top_5_data_pattern": [
{
"pattern": "XXXXXX",
"count_pattern": "501",
"pattern_ratio": "0.501"
},
{
"pattern": "XXXX",
"count_pattern": "499",
"pattern_ratio": "0.499"
}
]
},
"Product_line": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "6",
"ratio_distinct_value": "0.006",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "17",
"data_lenght_max": "22",
"top_5_data_value": [
{
"data_value": "Fashion accessories",
"count_data_value": "178",
"value_ratio": "0.178"
},
{
"data_value": "Food and beverages",
"count_data_value": "174",
"value_ratio": "0.174"
},
{
"data_value": "Electronic accessories",
"count_data_value": "170",
"value_ratio": "0.17"
},
{
"data_value": "Sports and travel",
"count_data_value": "166",
"value_ratio": "0.166"
},
{
"data_value": "Home and lifestyle",
"count_data_value": "160",
"value_ratio": "0.16"
}
],
"top_5_data_pattern": [
{
"pattern": "XXXX XXX XXXXXXXXX",
"count_pattern": "334",
"pattern_ratio": "0.334"
},
{
"pattern": "XXXXXX XXX XXXXXX",
"count_pattern": "318",
"pattern_ratio": "0.318"
},
{
"pattern": "XXXXXXX XXXXXXXXXXX",
"count_pattern": "178",
"pattern_ratio": "0.178"
},
{
"pattern": "XXXXXXXXXX XXXXXXXXXXX",
"count_pattern": "170",
"pattern_ratio": "0.17"
}
]
},
"Unit_price": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "943",
"ratio_distinct_value": "0.943",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "4",
"data_lenght_max": "5",
"min_value": "10.08",
"max_value": "99.96",
"mean": "55.67",
"median": "55",
"mode": "83.77",
"variance": "701.97",
"std": "26.49",
"first_quartile": "33",
"third_quartile": "78",
"iqr": "45",
"minimum": "10.5",
"maximum": "100.5",
"negative_outliner_datapoint": "0",
"positive_outliner_datapoint": "5",
"top_5_data_value": [
{
"data_value": "83.77",
"count_data_value": "3.0",
"value_ratio": "0.003"
},
{
"data_value": "99.96",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "24.74",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "45.38",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "45.58",
"count_data_value": "2.0",
"value_ratio": "0.002"
}
],
"top_5_data_pattern": [
{
"pattern": "XX.XX",
"count_pattern": "903",
"pattern_ratio": "0.903"
},
{
"pattern": "XX.X",
"count_pattern": "97",
"pattern_ratio": "0.097"
}
]
},
"Quantity": {
"dataType": "int64",
"is_primary_key": "False",
"distinct_value": "10",
"ratio_distinct_value": "0.01",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "1",
"data_lenght_max": "2",
"min_value": "1",
"max_value": "10",
"mean": "5.51",
"median": "5",
"mode": "10",
"variance": "8.55",
"std": "2.92",
"first_quartile": "3",
"third_quartile": "8",
"iqr": "5",
"minimum": "0.5",
"maximum": "10.5",
"negative_outliner_datapoint": "0",
"positive_outliner_datapoint": "0",
"top_5_data_value": [
{
"data_value": "10",
"count_data_value": "119",
"value_ratio": "0.119"
},
{
"data_value": "1",
"count_data_value": "112",
"value_ratio": "0.112"
},
{
"data_value": "4",
"count_data_value": "109",
"value_ratio": "0.109"
},
{
"data_value": "5",
"count_data_value": "102",
"value_ratio": "0.102"
},
{
"data_value": "7",
"count_data_value": "102",
"value_ratio": "0.102"
}
],
"top_5_data_pattern": [
{
"pattern": "X",
"count_pattern": "881",
"pattern_ratio": "0.881"
},
{
"pattern": "XX",
"count_pattern": "119",
"pattern_ratio": "0.119"
}
]
},
"Tax": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "990",
"ratio_distinct_value": "0.99",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "3",
"data_lenght_max": "18",
"min_value": "0.51",
"max_value": "49.65",
"mean": "15.38",
"median": "12",
"mode": "4.15",
"variance": "137.1",
"std": "11.71",
"first_quartile": "6",
"third_quartile": "22",
"iqr": "16",
"minimum": "-2.0",
"maximum": "30.0",
"negative_outliner_datapoint": "137",
"positive_outliner_datapoint": "0",
"top_5_data_value": [
{
"data_value": "39.48",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "13.187999999999999",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "22.428",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "4.4639999999999995",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "4.154",
"count_data_value": "2.0",
"value_ratio": "0.002"
}
],
"top_5_data_pattern": [
{
"pattern": "XX.XXX",
"count_pattern": "259",
"pattern_ratio": "0.259"
},
{
"pattern": "X.XXX",
"count_pattern": "195",
"pattern_ratio": "0.195"
},
{
"pattern": "XX.XXXX",
"count_pattern": "127",
"pattern_ratio": "0.127"
},
{
"pattern": "X.XXXX",
"count_pattern": "107",
"pattern_ratio": "0.107"
},
{
"pattern": "XX.XX",
"count_pattern": "97",
"pattern_ratio": "0.097"
}
]
},
"Total": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "990",
"ratio_distinct_value": "0.99",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "4",
"data_lenght_max": "18",
"min_value": "10.68",
"max_value": "1042.65",
"mean": "322.97",
"median": "254",
"mode": "87.23",
"variance": "60459.6",
"std": "245.89",
"first_quartile": "124",
"third_quartile": "471",
"iqr": "347",
"minimum": "-49.5",
"maximum": "644.5",
"negative_outliner_datapoint": "131",
"positive_outliner_datapoint": "0",
"top_5_data_value": [
{
"data_value": "829.08",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "276.948",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "470.98800000000006",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "93.744",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "87.234",
"count_data_value": "2.0",
"value_ratio": "0.002"
}
],
"top_5_data_pattern": [
{
"pattern": "XXX.XXX",
"count_pattern": "358",
"pattern_ratio": "0.358"
},
{
"pattern": "XXX.XXXX",
"count_pattern": "166",
"pattern_ratio": "0.166"
},
{
"pattern": "XXX.XX",
"count_pattern": "120",
"pattern_ratio": "0.12"
},
{
"pattern": "XXX.XXXXXXXXXXXXXX",
"count_pattern": "106",
"pattern_ratio": "0.106"
},
{
"pattern": "XX.XXX",
"count_pattern": "91",
"pattern_ratio": "0.091"
}
]
},
"Date": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "89",
"ratio_distinct_value": "0.089",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "8",
"data_lenght_max": "9",
"top_5_data_value": [
{
"data_value": "2/7/2019",
"count_data_value": "20",
"value_ratio": "0.02"
},
{
"data_value": "2/15/2019",
"count_data_value": "19",
"value_ratio": "0.019"
},
{
"data_value": "1/8/2019",
"count_data_value": "18",
"value_ratio": "0.018"
},
{
"data_value": "3/2/2019",
"count_data_value": "18",
"value_ratio": "0.018"
},
{
"data_value": "3/14/2019",
"count_data_value": "18",
"value_ratio": "0.018"
}
],
"top_5_data_pattern": [
{
"pattern": "X/XX/XXXX",
"count_pattern": "677",
"pattern_ratio": "0.677"
},
{
"pattern": "X/X/XXXX",
"count_pattern": "323",
"pattern_ratio": "0.323"
}
]
},
"Time": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "506",
"ratio_distinct_value": "0.506",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "5",
"data_lenght_max": "5",
"top_5_data_value": [
{
"data_value": "19:48",
"count_data_value": "7",
"value_ratio": "0.007"
},
{
"data_value": "14:42",
"count_data_value": "7",
"value_ratio": "0.007"
},
{
"data_value": "17:38",
"count_data_value": "6",
"value_ratio": "0.006"
},
{
"data_value": "17:36",
"count_data_value": "5",
"value_ratio": "0.005"
},
{
"data_value": "19:44",
"count_data_value": "5",
"value_ratio": "0.005"
}
],
"top_5_data_pattern": [
{
"pattern": "XX:XX",
"count_pattern": "1000",
"pattern_ratio": "1.0"
}
]
},
"Payment": {
"dataType": "object",
"is_primary_key": "False",
"distinct_value": "3",
"ratio_distinct_value": "0.003",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "4",
"data_lenght_max": "11",
"top_5_data_value": [
{
"data_value": "Ewallet",
"count_data_value": "345",
"value_ratio": "0.345"
},
{
"data_value": "Cash",
"count_data_value": "344",
"value_ratio": "0.344"
},
{
"data_value": "Credit card",
"count_data_value": "311",
"value_ratio": "0.311"
}
],
"top_5_data_pattern": [
{
"pattern": "XXXXXXX",
"count_pattern": "345",
"pattern_ratio": "0.345"
},
{
"pattern": "XXXX",
"count_pattern": "344",
"pattern_ratio": "0.344"
},
{
"pattern": "XXXXXX XXXX",
"count_pattern": "311",
"pattern_ratio": "0.311"
}
]
},
"cogs": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "990",
"ratio_distinct_value": "0.99",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "4",
"data_lenght_max": "6",
"min_value": "10.17",
"max_value": "993.0",
"mean": "307.59",
"median": "242",
"mode": "83.08",
"variance": "54838.64",
"std": "234.18",
"first_quartile": "118",
"third_quartile": "449",
"iqr": "331",
"minimum": "-47.5",
"maximum": "614.5",
"negative_outliner_datapoint": "131",
"positive_outliner_datapoint": "0",
"top_5_data_value": [
{
"data_value": "789.6",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "263.76",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "448.56",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "89.28",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "83.08",
"count_data_value": "2.0",
"value_ratio": "0.002"
}
],
"top_5_data_pattern": [
{
"pattern": "XXX.XX",
"count_pattern": "534",
"pattern_ratio": "0.534"
},
{
"pattern": "XXX.X",
"count_pattern": "242",
"pattern_ratio": "0.242"
},
{
"pattern": "XX.XX",
"count_pattern": "185",
"pattern_ratio": "0.185"
},
{
"pattern": "XX.X",
"count_pattern": "39",
"pattern_ratio": "0.039"
}
]
},
"gross_margin_percentage": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "1",
"ratio_distinct_value": "0.001",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "18",
"data_lenght_max": "18",
"min_value": "4.76",
"max_value": "4.76",
"mean": "4.76",
"median": "5",
"mode": "4.76",
"variance": "0.0",
"std": "0.0",
"first_quartile": "5",
"third_quartile": "5",
"iqr": "0",
"minimum": "5.0",
"maximum": "5.0",
"negative_outliner_datapoint": "0",
"positive_outliner_datapoint": "1000",
"top_5_data_value": [
{
"data_value": "4.7619047619999995",
"count_data_value": "1000.0",
"value_ratio": "1.0"
}
],
"top_5_data_pattern": [
{
"pattern": "X.XXXXXXXXXXXXXXXX",
"count_pattern": "1000",
"pattern_ratio": "1.0"
}
]
},
"gross_income": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "990",
"ratio_distinct_value": "0.99",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "3",
"data_lenght_max": "18",
"min_value": "0.51",
"max_value": "49.65",
"mean": "15.38",
"median": "12",
"mode": "4.15",
"variance": "137.1",
"std": "11.71",
"first_quartile": "6",
"third_quartile": "22",
"iqr": "16",
"minimum": "-2.0",
"maximum": "30.0",
"negative_outliner_datapoint": "137",
"positive_outliner_datapoint": "0",
"top_5_data_value": [
{
"data_value": "39.48",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "13.187999999999999",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "22.428",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "4.4639999999999995",
"count_data_value": "2.0",
"value_ratio": "0.002"
},
{
"data_value": "4.154",
"count_data_value": "2.0",
"value_ratio": "0.002"
}
],
"top_5_data_pattern": [
{
"pattern": "XX.XXX",
"count_pattern": "259",
"pattern_ratio": "0.259"
},
{
"pattern": "X.XXX",
"count_pattern": "195",
"pattern_ratio": "0.195"
},
{
"pattern": "XX.XXXX",
"count_pattern": "127",
"pattern_ratio": "0.127"
},
{
"pattern": "X.XXXX",
"count_pattern": "107",
"pattern_ratio": "0.107"
},
{
"pattern": "XX.XX",
"count_pattern": "97",
"pattern_ratio": "0.097"
}
]
},
"Rating": {
"dataType": "float64",
"is_primary_key": "False",
"distinct_value": "61",
"ratio_distinct_value": "0.061",
"missing_value": "0",
"ratio_missing_value": "0.0",
"data_lenght_min": "3",
"data_lenght_max": "4",
"min_value": "4.0",
"max_value": "10.0",
"mean": "6.97",
"median": "7",
"mode": "6.0",
"variance": "2.95",
"std": "1.72",
"first_quartile": "6",
"third_quartile": "8",
"iqr": "2",
"minimum": "5.0",
"maximum": "9.0",
"negative_outliner_datapoint": "151",
"positive_outliner_datapoint": "153",
"top_5_data_value": [
{
"data_value": "6.0",
"count_data_value": "26.0",
"value_ratio": "0.026"
},
{
"data_value": "6.6",
"count_data_value": "24.0",
"value_ratio": "0.024"
},
{
"data_value": "9.5",
"count_data_value": "22.0",
"value_ratio": "0.022"
},
{
"data_value": "4.2",
"count_data_value": "22.0",
"value_ratio": "0.022"
},
{
"data_value": "5.1",
"count_data_value": "21.0",
"value_ratio": "0.021"
}
],
"top_5_data_pattern": [
{
"pattern": "X.X",
"count_pattern": "995",
"pattern_ratio": "0.995"
},
{
"pattern": "XX.X",
"count_pattern": "5",
"pattern_ratio": "0.005"
}
]
}
}
Attrubute: sakda.auditing_config
Return type: dict
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config) print(get_profile.auditing_config)
Output
{'audit': {'audit_missing_value': False, 'define_custom_missing_value': ['.', '999'], 'audit_data_pattern': [{'column_name': 'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name': 'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'}], 'audit_outlier': False, 'audit_primary_key': False, 'audit_data_range': [{'column_name': 'Quantity', 'min': 0, 'max': 100}]}}
Attrubute: sakda.audited_result
Return type: dict
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config) print(get_profile.audited_result)
Output
{'audit_result': {'file_name': 'sample_supermarket_sales', 'audit_datetime': '23/09/2020 11:03:04', 'audit_data_range': [{'column_name': 'Quantity', 'range': {'min': 0, 'max': 100}, 'pass': 1000, 'not_pass': 0, 'pass_ratio': 1.0}], 'audit_data_pattern': [{'regex_pattern': '(Female|Male)', 'column_name': 'Gender', 'pass': 1000, 'not_pass': 0, 'pass_ratio': 1.0}, {'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})', 'column_name': 'Invoice_ID', 'pass': 1000, 'not_pass': 0, 'pass_ratio': 1.0}], 'overall_pass': 1000, 'overall_pass(%)': 1.0}}
Attrubute: sakdat.audited_result_json
Return type: string
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config) print(get_profile.audited_result_json)
Output
{
"audit_result": {
"file_name": "sample_supermarket_sales",
"audit_datetime": "23/09/2020 11:04:22",
"audit_data_range": [
{
"column_name": "Quantity",
"range": {
"min": 0,
"max": 100
},
"pass": 1000,
"not_pass": 0,
"pass_ratio": 1.0
}
],
"audit_data_pattern": [
{
"regex_pattern": "(Female|Male)",
"column_name": "Gender",
"pass": 1000,
"not_pass": 0,
"pass_ratio": 1.0
},
{
"regex_pattern": "(^[0-9]{3}-[0-9]{2}-[0-9]{4})",
"column_name": "Invoice_ID",
"pass": 1000,
"not_pass": 0,
"pass_ratio": 1.0
}
],
"overall_pass": 1000,
"overall_pass(%)": 1.0
}
}
Attrubute: dataset.audited_dataset_with_tag
Return type: pandas.core.frame.DataFrame
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config) print(get_profile.audited_dataset_with_tag)
Output
--
Attrubute: sakda.audited_dataset
Return type: pandas.core.frame.DataFrame
Example
from sakdas import Sakda as sd import pandas as pd df = pd.read_csv('<Path-to-File>/supermarket_sales.csv') auditing_config = {'audit':{ 'audit_missing_value': False, 'audit_data_pattern':[ {'column_name':'Gender', 'regex_pattern': '(Female|Male)'}, {'column_name':'Invoice_ID', 'regex_pattern': '(^[0-9]{3}-[0-9]{2}-[0-9]{4})'} ], 'audit_outlier': False, 'audit_data_range' : [{'column_name':'Quantity', 'min': 0, 'max': 100}] } } get_profile = sd(df,'sample_supermarket_sales','<Path-to-Result>', auditing_config = auditing_config) print(get_profile.audited_dataset)
Output
--
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