refineryframe 0.0.1

Cleans data, best to be used as a part of initial preprocessor

refineryframe

Under construction! Not ready for use yet! Currently experimenting and planning!

Initial plans

Goal of the package is to simplify life for data scientist that have to deal with imperfect raw data. The pachage suppose to detect and clean unexpected values, while doubling as safeguard in production code based on predifined conditions that arise from business assumptions or any other source. The package is well suited to be an initial preprocessing step in ml pipelines situated between data gathering and training/scoring steps.

Developed by Kyrylo Mordan (c) 2023

Package usage example

Creating example data (exceptionally messy dataframe)

df = pd.DataFrame({
    'num_id' : [1, 2, 3, 4, 5],
    'NumericColumn': [1, -np.inf, np.inf,np.nan, None],
    'NumericColumn_exepted': [1, -996, np.inf,np.nan, None],
    'NumericColumn2': [None, None, 1,None, None],
    'NumericColumn3': [1, 2, 3, 4, 5],
    'DateColumn': pd.date_range(start='2022-01-01', periods=5),
    'DateColumn2': [pd.NaT,pd.to_datetime('2022-01-01'),pd.NaT,pd.NaT,pd.NaT],
    'DateColumn3': ['2122-05-01',
                    '2022-01-01',
                    '2021-01-01',
                    '1000-01-09',
                    '1850-01-09'],
    'CharColumn': ['Fół', None, np.nan, 'nót eXpęćTęd', '']
})

df

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	num_id	NumericColumn	NumericColumn_exepted	NumericColumn2	NumericColumn3	DateColumn	DateColumn2	DateColumn3	CharColumn
0	1	1.0	1.0	NaN	1	2022-01-01	NaT	2122-05-01	Fół
1	2	-inf	-9997.0	NaN	2	2022-01-02	2022-01-01	2022-01-01	None
2	3	inf	inf	1.0	3	2022-01-03	NaT	2021-01-01	NaN
3	4	NaN	NaN	NaN	4	2022-01-04	NaT	1000-01-09	nót eXpęćTęd
4	5	NaN	NaN	NaN	5	2022-01-05	NaT	1850-01-09

Defining specification for the dataframe

MISSING_TYPES = {'date_not_delivered': '1850-01-09',
                 'date_other_missing_type': '1850-01-08',
                 'numeric_not_delivered': -999,
                 'character_not_delivered': 'missing'}

unexpected_exceptions = {
    "col_names_types": "NONE",
    "missing_values": ["NumericColumn_exepted"],
    "missing_types": "NONE",
    "inf_values": "NONE",
    "date_format": "NONE",
    "duplicates": "ALL",
    "date_range": "NONE",
    "numeric_range": "NONE"
}

replace_dict = {-996 : -999,
                "1000-01-09": "1850-01-09"}

Initializing TnS class

tns = Refiner(dataframe = df,
          replace_dict = replace_dict,
          unexpected_exceptions = unexpected_exceptions)

function for detecting column types

tns.get_type_dict_from_dataframe()

{'num_id': 'int64',
 'NumericColumn': 'float64',
 'NumericColumn_exepted': 'float64',
 'NumericColumn2': 'float64',
 'NumericColumn3': 'int64',
 'DateColumn': 'datetime64[ns]',
 'DateColumn2': 'datetime64[ns]',
 'DateColumn3': 'object',
 'CharColumn': 'object'}

adding expected types

types_dict_str = {'num_id' : 'int64', 
                   'NumericColumn' : 'float64', 
                   'NumericColumn_exepted' : 'float64', 
                   'NumericColumn2' : 'float64', 
                   'NumericColumn3' : 'int64', 
                   'DateColumn' : 'datetime64[ns]', 
                   'DateColumn2' : 'datetime64[ns]', 
                   'DateColumn3' : 'datetime64[ns]', 
                   'CharColumn' : 'object'}

moulding types

tns.set_types(type_dict = types_dict_str)

tns.get_type_dict_from_dataframe()

{'num_id': 'int64',
 'NumericColumn': 'float64',
 'NumericColumn_exepted': 'float64',
 'NumericColumn2': 'float64',
 'NumericColumn3': 'int64',
 'DateColumn': 'datetime64[ns]',
 'DateColumn2': 'datetime64[ns]',
 'DateColumn3': 'datetime64[ns]',
 'CharColumn': 'object'}

Check independent conditions

tns.check_missing_types()
tns.check_missing_values()
tns.check_inf_values()
tns.check_col_names_types()
tns.check_date_format()
tns.check_duplicates()
tns.check_numeric_range()

WARNING:TnS:Column DateColumn3: (1850-01-09) : 2 : 40.00%
WARNING:TnS:Column NumericColumn: (NA) : 2 : 40.00%
WARNING:TnS:Column NumericColumn_exepted: (NA) : 2 : 40.00%
WARNING:TnS:Column NumericColumn2: (NA) : 4 : 80.00%
WARNING:TnS:Column DateColumn2: (NA) : 4 : 80.00%
WARNING:TnS:Column CharColumn: (NA) : 2 : 40.00%
WARNING:TnS:Column NumericColumn: (INF) : 2 : 40.00%
WARNING:TnS:Column NumericColumn_exepted: (INF) : 1 : 20.00%
WARNING:TnS:Column DateColumn2 has non-date values or unexpected format.

Using the main function to detect unexpected values

tns.detect_unexpected_values(earliest_date = "1920-01-01",
                         latest_date = "DateColumn3")

DEBUG:TnS:=== checking column names and types
DEBUG:TnS:=== checking for presence of missing values
WARNING:root:Column CharColumn: (NA) : 2 : 40.00%
WARNING:root:Column DateColumn2: (NA) : 4 : 80.00%
WARNING:root:Column NumericColumn: (NA) : 2 : 40.00%
WARNING:root:Column NumericColumn2: (NA) : 4 : 80.00%
DEBUG:TnS:=== checking for presence of missing types
WARNING:root:Column DateColumn3: (1850-01-09) : 2 : 40.00%
DEBUG:TnS:=== checking propper date format
WARNING:root:Column DateColumn2 has non-date values or unexpected format.
DEBUG:TnS:=== checking expected date range
WARNING:root:** Not all dates in DateColumn are later than DateColumn3
WARNING:root:Column DateColumn : future date : 4 : 80.00%
DEBUG:TnS:=== checking for presense of inf values in numeric colums
WARNING:root:Column NumericColumn: (INF) : 2 : 40.00%
WARNING:root:Column NumericColumn_exepted: (INF) : 1 : 20.00%
DEBUG:TnS:=== checking expected numeric range
WARNING:TnS:Percentage of passed tests: 58.33%

tns.duv_score

0.5833333333333334

Using function to replace unexpected values with missing types

tns.replace_unexpected_values(numeric_lower_bound = "NumericColumn3",
                                numeric_upper_bound = 4,
                                earliest_date = "1920-01-02",
                                latest_date = "DateColumn2",
                                unexpected_exceptions = {"irregular_values": "NONE",
                                                            "date_range": "DateColumn",
                                                            "numeric_range": "NONE",
                                                            "capitalization": "NONE",
                                                            "unicode_character": "NONE"})

DEBUG:TnS:=== replacing missing values in category cols with missing types
DEBUG:TnS:=== replacing all upper case characters with lower case
DEBUG:TnS:=== replacing character unicode to latin
DEBUG:TnS:=== replacing missing values in date cols with missing types
DEBUG:TnS:=== replacing missing values in numeric cols with missing types
DEBUG:TnS:=== replacing values outside of expected date range
DEBUG:TnS:=== replacing values outside of expected numeric range
DEBUG:TnS:** Usable values in the dataframe:  44.44%
DEBUG:TnS:** Uncorrected data quality score:  32.22%
DEBUG:TnS:** Corrected data quality score:  52.57%

tns.dataframe

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	num_id	NumericColumn	NumericColumn_exepted	NumericColumn2	NumericColumn3	DateColumn	DateColumn2	DateColumn3	CharColumn
0	1	1.0	1.0	-999.0	1	2022-01-01	1850-01-09	1850-01-09	fol
1	2	-999.0	-999.0	-999.0	2	2022-01-02	2022-01-01	2022-01-01	missing
2	3	-999.0	-999.0	-999.0	3	2022-01-03	1850-01-09	1850-01-09	missing
3	4	-999.0	-999.0	-999.0	4	2022-01-04	1850-01-09	1850-01-09	not expected
4	5	-999.0	-999.0	-999.0	-999	2022-01-05	1850-01-09	1850-01-09	missing

tns.detect_unexpected_values(unexpected_exceptions = {
    "col_names_types": "NONE",
    "missing_values": "NONE",
    "missing_types": "ALL",
    "inf_values": "NONE",
    "date_format": "NONE",
    "duplicates": "ALL",
    "date_range": "NONE",
    "numeric_range": "NONE"
})

DEBUG:TnS:=== checking column names and types
DEBUG:TnS:=== checking for presence of missing values
DEBUG:TnS:=== checking propper date format
DEBUG:TnS:=== checking expected date range
DEBUG:TnS:=== checking for presense of inf values in numeric colums
DEBUG:TnS:=== checking expected numeric range

Scores

print(f'duv_score: {tns.duv_score :.4}')
print(f'ruv_score0: {tns.ruv_score0 :.4}')
print(f'ruv_score1: {tns.ruv_score1 :.4}')
print(f'ruv_score2: {tns.ruv_score2 :.4}')

duv_score: 1.0
ruv_score0: 0.4444
ruv_score1: 0.3222
ruv_score2: 0.5257